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# 52012SC0299

**COMMISSION STAFF WORKING DOCUMENT European Competitiveness Report 2012: Reaping the benefits of globalization Accompanying the document Communication from the Commission to the European Parliament, the Council, the Economic and Social Committee and the Committee of the Regions Industrial Policy Communication Update: A stronger European Industry for Growth and Economic Recovery /\* SWD/2012/0299 final \*/**

  

Table
of content

3.
Energy content in exports and
eco-innovation.. 91

3.1. Energy Efficiency from an Economic
Perspective. 92

3.2. Energy content in exports and
globalisation.. 96

3.2.1. Energy content in total exports. 96

3.2.2. Energy content in manufacturing and service exports. 101

3.2.3. Globalisation and the energy content in exports
worldwide. 109

3.2.5 Domestic-energy inputs vs domestic inputs in exports. 119

3.2.6 Measuring energy efficiency in the manufacturing
sector. 121

3.3. ECO-INNOVATION ADOPTION AND
THE COMPETITIVENESS OF EU FIRMS. 128

3.3.1. Background and literature review.. 128

3.3.2. Adoption of energy-saving technologies. 131

3.3.3 Market success of energy-efficiency product innovators. 132

3.4. Policy Implications. 138

3. Energy content in exports and eco-innovation

The prices of energy commodities, particularly
oil, have risen sharply in the last decade (see Figure 3.1). Some of the causes
are structural, such as globalisation and the increasing demand from developing
countries, limited fossil-fuel resources and an overall increase in exploration
costs, and these tend to lead to permanent energy-price increases. Cyclical
factors such as the considerable rigidity of energy demand in the short term;
the failure to fully anticipate its fast growth, as shown by preceding low
levels of exploration investment and spare capacity; or concerns related to
geopolitical events were often the major causes behind some of the recurrent
energy price hikes and volatility observed. In addition there has been a
significant increase in financial investment flows into energy commodity
derivative markets. While the debate on the relative importance of the multiple
factors influencing energy prices is still open, it is clear that energy commodity
markets have become more closely linked to financial markets.

Figure 3.1 – Crude oil
spot prices (USD/barrel)

Source: IMF.

Rising energy price and volatility levels have a
series of potential effects on businesses, production costs, economic activity
or external accounts and competitiveness. These effects will be larger for
countries or sectors that are less energy-efficient, more specialised in energy-intensive
products or more energy-dependent (e.g. countries more heavily dependent on
imported fossil fuels).

This chapter studies the energy content in
exports and energy-efficiency trends over the past 15 years in the
context of key economic developments such as the globalisation of industrial activities,
investments in energy-efficient technologies and eco-innovation. Their impact
on competitiveness is analysed at country, sector and firm level. Section 3.2
analyses the developments and the improvements in overall energy productivity
and investments in more energy-efficient technologies at an international
level. Section 3.3 analyses the interplay between the trends in the energy
content in exports and globalisation, their impact on competitiveness and the
prominent role played by industry and services. This is a novel integrated
analysis (mapping) of energy use per sector at domestic and global levels based
on the World Input Output Database (WIOD) made available recently. Section 4
analyses the evidence for the adoption and development of eco-innovations by EU
firms and how this translates into performance and competitiveness, focusing on
energy-efficiency process technologies and products. Section 5 draws conclusions.

              3.1. Energy Efficiency from an Economic Perspective

This section provides a short analysis of the
global trends in energy efficiency in the last 15 years using the World Input
Output Database (WIOD). A cross-country comparison of energy-efficiency
performance makes it possible to identify and introduce such related key
economic developments as the internationalisation of production chains or
investments in energy-efficient technologies, underpinning the more detailed
analyses (at country, sector and firm level) that follow in the other sections.

The WIOD accounts for approximately 85 percent
of the world’s production. The world input-output data is reported for 41
countries (the EU-27 countries, 13 other major world economies and the rest of
the world) and 35 sectors (NACE rev. 1) over the period
1995-2009 (see Box 2.1 in Chapter 2 of this report). Most importantly for this
chapter, the economic data is linked to environmental accounts and energy use.
The WIOD database considers the use-side of energy and reports ‘gross energy
use’ covering the transformation of primary energy into other forms of energy
like electricity and heat, as well as the final use of energy. Energy is
reported in terajoules of crude-oil inputs. As a general rule, throughout this
chapter the other economic variables used to compute energy-efficiency
indicators and ratios are first transformed into constant prices.

Figure 3.2 shows the patterns of
energy consumption and economic output (per capita) for the European Union and
its most important competitors (as well as separately for a selection of Member
States: Bulgaria, Ireland and the Netherlands). Countries’
per capita GDP are plotted against the amount of energy per capita that was
used to produce per capita GDP (PPP adjusted GDP was considered to be closer to
the real level of economic activity and output). The figure also shows energy-efficiency
improvements over time. Country-level observations for 1995 are
indicated by light colours. The more recent an observation is, the darker it is
plotted.

Figure 3.2 – GDP and
Energy Use per Capita (1995 – 2009)

Note: Bulgaria (BG), Ireland (IE), United States
(US), Japan (JP), China (CN), (South) Korea (KR), Taiwan (TW), Canada (CA),
Australia (AU), Turkey (TR), Brazil (BR), India (IN), Mexico (MX), Indonesia
(ID), and Russia (RU). Source: WIOD.

A measure of energy productivity (a
crude measure of energy efficiency) is indicated by the slope of grey dotted
lines. The steeper the line the higher the energy productivity, meaning that
less energy per capita is used to produce a unit of GDP per capita. In 2009,
energy productivity was highest in Ireland and lowest in Russia (comparing the
two grey dotted lines at their 2009 values, using one gigajoule of energy one
person in Ireland is able to produce goods and services with a value of USD 215,
4 times more than in Russia — USD 49  — using the same amount of energy). It
has to be noted that using purchasing power parities rates (instead of exchange
rates) increases the value of GDP — and therefore measured energy productivity  —
in countries with a low cost of living. Overall PPP adjustment narrows the gap
in measured energy productivity between countries and regions, but leaves the
trends unchanged.

Energy efficiency improved overall
in the period 1995-2009 in advanced economies (the decline in measured energy
productivity in 2008 and 2009 in some countries can to a large extent be
explained by cyclical low capacity utilisation associated with the economic
crisis). The European Union and Japan reinforced their lead in terms of energy
productivity. EU-12 countries as a whole significantly narrowed their gap in
energy efficiency vis-à-vis the EU-15 (Bulgaria is one of the EU Member States
with the lowest energy-productivity levels). Conversely, in countries like China, India, Taiwan and Korea energy-efficiency improvements from 1995 until 2009 are much less
perceptible.

Energy is used in practically all production
processes and the importance of energy efficiency as a competitiveness factor
is growing over time with globalisation. The globalisation of industrial
activities tends overall to exert pressure to improve energy efficiency and
speed-up the convergence of energy productivity in industry across countries.
As result, significant economic changes and differentiated impacts on the
competitiveness of different countries and sectors are to be expected. Section
3 analyses the changes in the energy content in exports in the context of the
increasing global trade in intermediates and the internationalisation of
production networks.

Rising energy prices and volatility levels were
major underlying drivers for the changes observed in energy use and the overall
improvement in energy productivity. Permanent increases in energy prices and
volatility levels lead to significant economic changes, in particular in terms
of energy-saving efforts and investments in energy-efficient technologies. The
search for energy savings includes choosing products and services with less
energy content and more energy-efficient production technologies. A prominent
example is the development and use of more energy-efficient consumer durables
and capital goods. Typically, they are the result of investment decisions
comparing higher initial capital costs with expected future savings in energy
operating costs. This example also provides a straightforward illustration of
the well-known limitations in energy-efficiency the improvements in the short
run (due, for example, to the long lifetimes of the capital equipment) versus a
higher degree of responsiveness in the medium and long run[1].

The WIOD data is now linked to country-level
data from the Penn World Tables 7.0.[2]
Figure 3.3 plots energy use against the countries’ physical capital stock (both
energy
use and the physical capital stock are scaled by the GDP). The y-axis
reports the countries’ energy intensity, meaning the quantity of energy (in
gigajoules) needed to produce 1 US dollar (at 2005 prices) of GDP. The x-axis
indicates capital intensity, i.e. the dollar value of the capital stock of a
country that was needed to produce 1 US dollar of GDP. Only a selection of
countries is presented for the sake of illustration (Australia, India, and Brazil are no longer included in the figure due to visual overlap). Again, country-level observations
for 1995 are indicated by light colours. The more recent an observation is, the
darker it is plotted.

Figure 3.3 – Capital Stock
and Energy Use per GDP (1995 – 2009)

SS

Source: WIOD, Penn World Tables
7.0.

China has reduced both energy use and capital
use to produce one dollar of GDP over time. In other countries (including also
the European Union), a shift towards less energy intensive and more capital-intensive
production tends to be observed. This overall trend of the substitution of
energy by capital reflects the choice at aggregate level for more energy-efficient
technologies embodied in capital goods following the overall increase in the
international price of energy observed in the period up to 2008 (see Figure
3.1).

The aggregate analysis just made applies
similarly at the sectoral, firm or household levels. Permanent increases in
energy prices are one of the factors exerting strong pressure for the adoption
of more energy-efficient technologies, the replacement of older capital
equipment and the attraction of new entrants (Linn, 2008), as well as inducing
the development of energy-efficiency eco-innovations over the medium and long
term. Popp (2002) identified increasing prices of energy in the oil crisis as
the significant driver of energy-saving inventions (energy-related patent
applications appear to respond with a lag). Newell et al. (1999) provide
evidence of price-induced eco-innovation in new air conditioners. Jaffe and
Stavins (1995) find noticeable impacts on the adoption of energy-efficient
technology for buildings. Energy efficiency and eco-innovation can be promoted
through a broad range of public policies and instruments such as regulations
and standards, eco-design, eco-labels, energy taxes and subsidies. Evidence on
energy efficiency and eco-innovations adoption and its impact on the competitiveness
of EU firms are analysed in section 3.5 (using firm-level data from the
European Community Innovation Survey).

              3.2. Energy content in exports and globalisation

Increasing global
competition and integration of production chains (involving more and more
economic activities and tasks and covering new countries and geographical
areas) are developments with far-reaching social, political and economic
consequences. Global competition and off-shoring have an enormous potential and
offer new opportunities in terms of the efficient exploitation of existing
technologies and resources. The development and adoption of eco-innovations
tend also be fostered by global competition[3]. As a
result, greater energy-efficiency improvements can be expected within and
across firms, sectors and countries, helping to achieve environmental and
climate change goals world-wide.

However, the quest for economic efficiency does
not necessarily translate into energy efficiency and related environmental
efficiency. Market failures (in energy or other markets) or regulatory failures
may stand in the way and impair the simultaneous achievement of eco-efficiency,
in particular on a world-wide basis. For example, various stages of production
may be offshored to less energy-efficient countries or firms as a result of
distorting taxes or subsidies on energy products. Existing plants in
pollution-intensive industries can be relocated to regions with less stringent
or unenforced regulations. Some evidence for this is pre­sented by Henderson (1996) (see also List, Millimet, Fredriksson and McHone (2003); a survey of this
strand of the literature is offered by Brunnermeier and Levinson (2004)).

A fully-fledged analysis of these complex issues
is beyond the scope of this chapter. This section merely investigates the
relationship between the internationalisation of production and changes in the
energy content in exports, focusing on the EU, US and Japan. The main interest is in analysing (mapping) the energy use for exports in terms of its
sources: domestic intermediates versus foreign intermediates (focusing on the
energy content of exports — via embodied energy in intermediate imports). The
role and different impacts on manufacturing and service exports are also
analysed. The contribution of improved technical efficiency in the
manufacturing sector to overall energy efficiency and competitiveness is also
briefly analysed using a standard decomposition method.

              3.2.1. Energy content in total
exports

Input-output tables and in particular the WIOD
database (which, as mentioned, contains detailed information on international
and inter-industry transactions, for N=35 industries and C=41 economies – including
the rest of the world – from 1995 to 2009) make it possible to trace the source
and the energy content of goods and services produced in vertically-integrated
industries and cross-border production networks. This provides an integrated
global framework for the analysis of energy use that does not suffer from the
limitations of standard sectoral or purely domestic input output data which do
not take the interlinkages between sectors/countries into account.

Suppose there was interest to trace the energy
inputs (per sector and country) and to calculate the energy content of a German
car exported to China. The energy (e.g. electricity) used directly in the car-manufacturer’s
plant would be one element. To that must be added the series of (indirect)
energy consumptions embodied in the car components purchased by the
manufacturer (e.g. the electricity used in the mining industry in Australia or in the production of the intermediates purchased from the electronics industry in Germany or other countries). The inverse Leontief matrix (from the input-output tables) can
be used to calculate the total energy inputs (direct and indirect, in all
rounds of production of the car and car components).

With data on energy use by industry, the
Leontief inverse matrix can be pre-multiplied by the energy coefficients vector
(i.e. energy used per unit of output) and post-multiplied by the vector of
exports. This then allows a separation of the energy directly and indirectly
used by a partner country to produce another country’s exports and its domestic
energy use. The calculation of energy-input coefficients (i.e. energy use per
unit of gross output) was performed using deflated gross output series. Gross
output was deflated to constant 1995 prices, using industry-level price indices
for each country.

The energy embodied in country r exports
(measured in terajoule, TJ) is given by

where e denotes the NCx1 vector of energy use
per unit of gross output (measured in constant prices, the prime denotes
transposition),  is the
inverse Leontief matrix and  the
NCx1 vector with country r exports (see Box 2.1 in Chapter 2 of this report).

The left-hand panel in figure 3.4 shows an index
of the energy embodied in exports for EU-15, EU-12, Japan and the US, over the period 1995-2009. Total energy inputs in exports increased globally in the
four economies in the pre-crisis period (between roughly 130% in the US and 180% in the EU-15 up to 2007). In 2008-2009 the energy embodied in exports declined
significantly and globally as a result of the economic crisis and the collapse
in worldwide trade. The impact of the crisis and the sudden reversal of the
long term upward trends in global trade can be seen in the right-hand panel in Figure
3.4 (presenting the underlying trade trends in terms of the index for total
exports, for each of the four economies over the whole period 1995-2009).

Figure 3.4 – Indexes (1995=100): total energy
embodied in exports (left panel) and total exports (right panel), 1995–2009

Source: WIOD.

The growth of total exports was higher in the EU
overall (in particular the EU-12) than in Japan and in the US over the period analysed. The significant increase in total exports in the EU-12 economies as a
whole is to a large extent due to their relatively high and increasing degree
of vertical specialisation (e.g. in their role as providers of intermediates
namely to EU-15, as documented in section 2.3.2 of the second chapter in this
report, see e.g. Figure 2.1). This fact is corroborated by the much less than
proportional growth rate in the energy embodied in exports (observed in the
left-hand panel of Figure 3.4) for the EU-12.

A slight opposite trend occurs in Japan, for which the increase in energy inputs was slightly higher than the growth in the
underlying total exports. In part, this may be due to the specialisation of the
Japanese economy and eventually to its relatively high degree of vertical
specialisation and its integration links with the Chinese economy (see, for
example, Table 3.1 below or Figure 2.2. in Chapter 2 of this report). For the
other two advanced economies (the EU-15 and the US), the underlying growth in
total exports has been accompanied by a (broadly) a more proportional variation
in the energy embodied.

This can be observed in Figure 3.5, presenting
the energy embodied per unit of total exports for the four economies over the
same period. In the left-hand panel, the marked decline in the total energy
inputs per unit of exports in the EU-12 (and only to a much smaller extent in
the EU-15) contrasts with the increase in the energy content in Japanese
exports and the relative stagnation observed in the US for the whole period.
The EU-15 and Japan lead in terms of the lowest energy content in exports but
the catching-up achieved by the EU-12 over the period is noticeable.

The right-hand panel in Figure 3.5 depicts the
energy embodied per unit of exports that is sourced domestically in each of the
four economies (i.e. the sum of the energy incorporated by each of the 35
domestic sectors in all the various implicit rounds, stages of production and
embedded economic activities in the achievement of the total exports of goods,
services, raw materials and intermediates)[4].

Figure 3.5 – Energy embodied (TJ) per unit of
exports (USD million), 1995–2009

Source: WIOD.

The energy embodied per unit of exports that is
sourced domestically is dominant in all four economies (particularly in the US, given the similarity in size of the respective columns (bars) in the two panels in Figure
3.5). Over time, the domestic energy embodied in exports and the overall energy
content tend to move in parallel to a large extent but some differences can be
noticed. For the EU-15 and EU-12, for instance, the observed drop in the
domestic component of the energy content in exports is more pronounced than the
decline in the total energy embodied, reflecting the rising importance of
foreign sources in the energy embodied in exports. As a result, the EU-15
caught up Japan in 2007 (and outperformed it in 2009) in terms of the lowest
domestic energy content in exports.

One of the effects of the increasing
cross-border integration of production networks can be seen in the rising
importance of foreign economies as a source of the energy inputs embodied in
exports. Figure 3.6 presents the share of foreign energy inputs embodied in
exports[5]. The energy
content in exports sourced from foreign countries rose continuously in all four
economies up to 2007, but at a slower pace in Japan and the US. In the US, the domestic component is more important, representing more than 80% of the overall
energy content in exports, partly reflecting the USA’s lower dependence in
terms of imported fossil fuels compared to the other three economies overall (in
2009 the domestic energy shares were 72%, 66% and 67% in the EU-12, EU-15 and
Japan, respectively).

Figure 3.6 – Share of foreign energy embodied in
exports, (percentage 1995–2009)

Source: WIOD.

This is unlike the pattern observed in Figure
2.1 (Chapter 2 of this report) in which the EU-12 had a higher level of import
content in exports relative to the EU-15, Japan and the US (the reasons are
discussed in Chapter 2, namely the openness of the EU-12 — being a group of
small and medium-sized countries —  and their vertical-integration links in
particular with the EU-15). This contrasts with broadly identical levels of
foreign-energy content in exports for the EU-15 and EU-12 (and Japan in the later years) observed in Figure 3.6. Another distinctive feature is apparent
in Table 3.1. It concerns the greater weight overall of energy-rich economies
(such as some countries in BRII and ROW) in terms of foreign-energy content
relative to import content in exports (see also subsection 3.3.3 and Figure
3.16 below).

Table 3.1 presents a detailed breakdown of the
sourcing structure of embodied energy inputs in exports (the domestic component
is highlighted in grey). The changes over time and the geographical patterns
follow expectations for each of the four economies. In the EU-12, the considerable
reduction (by almost 20 percentage points in the period 1995-2007) in the
domestic share of energy embodied in exports is mirrored in the large increases
in the weight of traditional trade and energy supplier partners (like the
EU-15, BRII — Brazil, Russia, India and Indonesia — and the Rest Of the World —
ROW) and China (and smaller increases in the shares of other trade partners).
In the period 1995-2007, all EU-12 trade partners in Table 3.1 steadily
increased their shares of the energy embodied in EU-12 exports (except Mexico and the US in 2005).

Table 3.1 – Geographic
(source) structure of energy embodied in exports (1995–2009, share in
percentage, domestic source highlighted in grey)

|| EU-12 || EU-15

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 5.0 || 6.6 || 6.8 || 8.4 || 6.4 || 3.7 || 4.0 || 6.0 || 7.4 || 6.8

Canada || 0.1 || 0.2 || 0.3 || 0.4 || 0.3 || 0.7 || 0.8 || 0.7 || 0.8 || 0.7

China || 0.3 || 1.1 || 2.9 || 4.7 || 6.1 || 1,6 || 2.2 || 3.4 || 4.8 || 6.5

EU-12 || 86,2 || 78.0 || 74.4 || 67.7 || 71.7 || 2.4 || 2.2 || 2.5 || 2.7 || 2.8

EU-15 || 4.5 || 6.9 || 7.8 || 8.8 || 7.1 || 79.4 || 75.0 || 72.4 || 66.5 || 65.8

Japan || 0.1 || 0.3 || 0.4 || 0.5 || 0.4 || 0.4 || 0.6 || 0.5 || 0.6 || 0.6

S. Korea || 0.1 || 0.3 || 0.5 || 0.8 || 0.8 || 0.3 || 0.6 || 0.6 || 0.8 || 0.8

Mexico || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.2 || 0.3 || 0.3 || 0.3 || 0.3

USA || 0.5 || 1.3 || 1.0 || 1.3 || 1.1 || 2.3 || 3.1 || 2.6 || 2.9 || 2.9

ROW || 3.2 || 5.3 || 5.8 || 7.3 || 6.0 || 9.0 || 11.3 || 11.0 || 13.3 || 12.8

|| || || || || || || || || ||

|| Japan || USA

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 4.4 || 4.7 || 5.2 || 6.1 || 4.7 || 1.4 || 1.8 || 2.4 || 2.4 || 2.1

Canada || 0.9 || 0.7 || 0.5 || 0.5 || 0.4 || 2.3 || 2.6 || 2.7 || 2.5 || 2.1

China || 3.1 || 4.0 || 7.6 || 7.9 || 8.5 || 1.6 || 1.9 || 3.4 || 3.7 || 4.7

EU-12 || 0.2 || 0.2 || 0.3 || 0.3 || 0.2 || 0.2 || 0.2 || 0.3 || 0.2 || 0.2

EU-15 || 2.1 || 2.0 || 2.1 || 1.8 || 1.3 || 1.7 || 1.9 || 2.2 || 2.0 || 1.6

Japan || 71.9 || 69.5 || 64.7 || 62.1 || 66.6 || 0.6 || 0.6 || 0.5 || 0.5 || 0.4

S. Korea || 2.4 || 3.2 || 2.8 || 2.4 || 1.7 || 0.5 || 0.6 || 0.6 || 0.7 || 0.6

Mexico || 0.1 || 0.2 || 0.1 || 0.2 || 0.1 || 0.6 || 0.8 || 1.0 || 1.0 || 1.2

USA || 2.9 || 3.3 || 2.6 || 2.5 || 1.6 || 86.0 || 83.9 || 81.5 || 81.2 || 81.5

ROW || 11.9 || 12.2 || 14.1 || 16.3 || 14.9 || 5.1 || 5.6 || 5.5 || 5.7 || 5.6

Source: WIOD. Note: BRII denotes
Brazil, Russia, India and Indonesia, ROW-Rest of the world.

The domestic proportion of the energy content in
EU-15 exports decreased steadily over the whole period (from 4/5 in 1995 to 2/3
in 2009) reflecting the increasing weights of the BRII economies, the ROW and
China. In 2009, China’s share of energy embodied in EU-15 exports was already
more than twice the — relatively stable —  share accounted for by traditional
trade partners like the EU-12 or the US. The other trade partners listed in the
table have smaller shares that increased slightly overall or tended to remain
relatively stable.

The increased importance of China as a source of energy content in exports globally is particularly striking in the case of Japan (accounting for more than 8% of the energy content in total exports in 2009). The
increase in China’s share, and to a smaller extent that of the ROW and the BRII
economies, almost compensates for the reduction in the domestic share in the
energy content in Japanese exports in the period 1995-2007. The shares of other
important Japanese trading partners like South Korea and the US remained fairly stable or decreased only slightly in the period 1995-2007.

The US maintained a relatively higher domestic
share of the energy content in exports and relatively lower shares for typical
energy-sourcing countries within the BRII and the ROW, partly reflecting the US’s lower dependence in terms of imported fossil fuels compared to overall the EU-15, EU-12 and Japan. China has comparatively a smaller share of the energy embodied in US exports and Canada has a more prominent weight in the US (relative to the EU-15, EU-12 and Japan).

The recent crisis together with its impact on
global trade, in particular for industries with more developed cross-border
production networks, led to a halt and in some cases a reversal of the previous
trends. Overall, the domestic content of energy embodied in exports started
rising at the expenses of the foreign content for the majority of trade
partners. The exception is China, which continued to increase its share for the
four economies analysed, squeezing the shares of other foreign economies. In
fact, China is the single economy whose share increased more over the whole
period for all the four economies analysed (China’s share increased by 5
percentage points or more for Japan, the EU-12 and EU-15 and by 3 percentage
points in the US in the period 1995-2009).

These developments are to a great extent the
result of the globalisation of production and underlying vertical-specialisation
trends observed in terms of the import content of exports in the second chapter
of this report (see, for example, Table 2.2). The analysis suggests that, along
with increasing globalisation, the EU economies (as a whole) have been able to
export more and at the same have reduced the energy embodied in their exports,
in particular the part that is sourced domestically. Overall, the EU economies
have been leading (relative to Japan and the US) in the reduction of the energy
content per unit of exports and in the global trends towards the increasing
weight of foreign-energy inputs in the total energy embodied in exports.
Services and manufacturing exports have played a central role in this process.
This is the subject of the analysis in the next subsection.

              3.2.2. Energy content in
manufacturing and service exports

Manufacturing transforms primary energy inputs
into final energy products and uses energy in the transformation of materials
into products; many manufacturing sectors are at the forefront of the
internationalisation of production networks.

Figure 3.7 highlights
the importance of manufacturing in terms of exports and how this is translated
into the energy embodied in exports for the four economies being analysed. The
right-hand panel shows that manufacturing exports accounted in the years
2007-2009 for around 80% of total exports in Japan, 70 % in the European
economies and 60 % in the US. The share of manufacturing in total exports
has been falling in all economies, except for the EU-12 (reflecting the
vigorous increase in manufacturing exports; to a great extent, this is the
result of the increasing vertical integration of the EU-12 documented in
Chapter 2 of this report). A number of manufacturing industries (e.g. producing
durable goods) were severely hit during the most recent crisis and the share of
manufacturing in total exports dropped in all economies in 2007-2009 except for
Japan, for which the exports of services declined more than manufacturing
exports during the crisis, see Figure 3.8 below.

Manufacturing activities
involve transforming a range of material inputs into products, so
manufacturing exports generally tend to have a higher energy content than total
exports. The share of energy embodied in manufacturing relative to total
exports (in the left-hand panel in Figure 3.7) is higher overall than the
weight of manufacturing in total exports. This is true for all four economies,
except for the EU-12 in 2009 and Japan in the years 1995, 2005, cases in which
the shares in the left-hand and right-hand panels in Figure 3.7 are roughly
identical.

Figure 3.7 – Energy
embodied in manufacturing exports relative to total energy embodied in total
exports (left panel) and share of manufacturing exports in total exports (right
panel), 1995–2009

Source: WIOD.

Moreover, the energy
embodied in manufacturing exports as a share of the energy embodied in total
exports remained broadly stable (or even increased slightly in some sub-periods
and for the whole period 1995-2009) while at the same time the share of
manufacturing exports fell overall. The exception was the EU-12, for which
manufacturing as a whole outperformed the overall reduction of energy content
in total exports.

Figure 3.8 illustrates the growing importance of
service exports and their overall lower energy content relative to
manufacturing exports. The right-hand panel shows that the share of services in
total exports has been growing for all economies in the last 15 years, except
in the EU-12 (for which manufacturing remained the dominant driver of export
growth). Altogether, manufacturing and services accounted for more than the 95 %
of total exports for all four economies (the highest share is reached in Japan, 99 % of total exports, see Table 3.4).

The growth of service exports was particular
strong in the European economies (+320 % in the EU-12 and +250 % in
the EU-15 in the period 1995-2007). In the EU-15, the growth of manufacturing
exports was much lower (around +150 % in the period 1995-2007) and as a
result the share of services in total exports rose from 20 % in 1995 to
close to 30 %. In 2007, the share of services accounted for more than 1/3
of total exports in the US and for around 20 % in the EU-12 and Japan. Japan has a much lower share than the US and the EU-15 in services such as financial
intermediation and Renting and Machinery and Equipment and other business
services (including ICT and R&D-related services). During the recent
crisis, exports dropped considerably in a number of service sectors (including
more cyclical-related sectors such as water transport and wholesale trade and
commission trade, NACE codes 61 and 51, respectively), leading to the observed
fall in the share of services in total exports in Japan.

Figure 3.8 – Energy embodied in service exports
relative to total energy embodied in total exports (left panel) and share of
service exports in total exports (right panel), 1995–2009

Source: WIOD. Note: Service
includes the sectors NACE rev. 1 codes 50 to P.

Not surprisingly, Figure
3.8 shows that service exports as a whole tend to have a relatively
lower energy content (the share of energy embodied in service exports relative
to total exports (left-hand panel) is lower overall than the weight of services
in total exports (right-hand panel)). Moreover, energy embodied in service
exports relative to total exports decreased (or remained broadly stable in the
case of EU-12 and US) while the share of service exports increased overall
(except in the crisis period 2007-2009 in the case of Japan and for the EU-12,
where growth in manufacturing exports dominated the whole period).

Table 3.2 presents energy embodied per unit of
exports (panel A) and the share of the energy inputs that is sourced from
foreign countries (panel B) for manufacturing, services and total exports (in
the latter case, a convenient recast of the data in Figures 3.4 and 3.6 above).

Panel B shows a steady rise in the share of
foreign-energy inputs in the total energy embodied in exports (both
manufacturing and services up to 2007). Partly reflecting a higher degree of
cross-border production linkages (see Chapter 2 of this report, Figure 2.2),
manufacturing has a higher share of foreign energy content relative to services
(except for the EU-12 in 1995). However, the gap between the share of foreign
energy in manufacturing and services narrowed, in particular in the EU-15. The
input-output linkages between services and manufacturing explain why the
differences between the two sectors are much smaller in terms of foreign-energy
content than in import content. Services source many of their more
energy-intensive inputs from manufacturing, some of which are in turn directly
and indirectly sourced from foreign countries.

Japan leads over the period 1995-2007 in terms
of the highest content of foreign energy inputs in exports. The US has overall a larger share of domestic-energy inputs in exports, particularly in
services.

Figure 3.9 plots the changes (in the period
1995-2007) against the level of the energy content in exports in 2007
(highlighting the main trends in the data presented in  panel A of Table 3.2).
Manufacturing is depicted by the larger bubbles. The EU-15 and Japan lead in terms of having the lowest energy content in services and manufacturing exports but
the energy content in manufacturing exports increased in the period 1995-2007,
particularly in Japan. The EU-15 kept the energy content in total exports
broadly constant in the period up to 2007 mainly thanks to a reduction in the
energy embodied in service exports (together with their greater and increasing
weight in total exports relative to Japan, see also Figure 3.8).

Table 3.2 – Energy
embodied (TJ) per unit of exports (USD million) (left panel) and share of
foreign energy embodied in exports (right panel) 1995–2009

|| (A) Energy inputs per unit of exports || || (B) Share of foreign energy inputs

|| 1995 || 2000 || 2005 || 2007 || 2009 || || 1995 || 2000 || 2005 || 2007 || 2009

|| Manufacturing (NACE D)

EU-12 || 63.6 || 38.0 || 34.8 || 30.0 || 27.3 || || 14% || 23% || 29% || 36% || 33%

EU-15 || 17.6 || 18.2 || 20.8 || 20.5 || 17.8 || || 23% || 27% || 29% || 34% || 35%

Japan || 11.1 || 12.1 || 16.7 || 19.5 || 20.1 || || 29% || 31% || 36% || 38% || 34%

USA || 25.9 || 23.8 || 29.0 || 31.8 || 28.6 || || 16% || 19% || 21% || 20% || 20%

|| Services (NACE 50 to P)

EU-12 || 31.4 || 26.7 || 29.1 || 22.0 || 20.8 || || 16% || 22% || 19% || 26% || 22%

EU-15 || 14.3 || 12.7 || 12.6 || 8.8 || 8.1 || || 13% || 19% || 22% || 32% || 33%

Japan || 10.9 || 12.1 || 13.1 || 12.1 || 10.8 || || 26% || 30% || 34% || 35% || 30%

USA || 14.4 || 15.8 || 17.9 || 16.0 || 11.0 || || 8% || 9% || 12% || 14% || 15%

|| .Total exports (NACE A to P)

EU-12 || 55.5 || 36.6 || 34.8 || 29.6 || 27.6 || || 14% || 22% || 26% || 32% || 28%

EU-15 || 17.0 || 16.9 || 18.8 || 17.4 || 14.9 || || 21% || 25% || 28% || 33% || 34%

Japan || 11.0 || 12.1 || 15.9 || 17.8 || 18.8 || || 28% || 30% || 35% || 38% || 33%

USA || 22.2 || 21.3 || 25.2 || 26.1 || 21.8 || || 14% || 16% || 19% || 19% || 19%

Source: WIOD.

Following its integration in cross-border
production networks and strengthening of its vertical specialisation, the EU-12
achieved a noticeable reduction and catching-up in the energy content of
manufacturing exports. The EU-12 reached the same energy content in
manufacturing exports as the US in 2007. The reduction in the energy content in
service exports was comparatively much smaller.

The energy content in the US increased both for manufacturing and service exports in the period 1995-2007 (in a broadly
similar trend to Japan’s). The higher energy content in US exports vis-à-vis the EU-15 and Japan is less pronounced in services. Combined with a larger
share of service exports in the US, this mitigates the gap in energy embodied
per unit of US total exports.

Figure 3.9 – Energy
content in exports (for manufacturing, services and total exports): change
1995-2007 versus level in 2007

Source: WIOD. Note: Manufacturing
is depicted by the larger bubbles. The size of the bubbles reflects the weight
of manufacturing and services in total exports in 2007. The points enclosed in
the small black circles of uniform size represent total exports.

Figure 3.10 presents the breakdown of energy
inputs per unit of exports by domestic and foreign countries’ sources. The
amount of foreign-energy inputs per unit of exports increased overall in all
four economies for both manufacturing and services in the period 1995-2007. In
the period 1995-2007, (as already observed in Figure 3.5 above), the domestic
energy content in total exports decreased in the European economies and
increased in Japan and to a lesser extent in the US. For the EU-12, this is due
to a significant drop in the energy incorporated domestically in manufacturing
exports and to a much lesser extent in service exports. In contrast, in the
EU-15 this is mainly the result of the considerable drop in the domestic-
energy content of service exports. As from 2007, the EU-15 also clearly leads
in terms of the lowest domestic-energy inputs per unit of service exports.
Regarding manufacturing exports, the EU-15’s domestic-energy content remained
constant and the increase in total energy embodied was due to the increase in
foreign-energy inputs. For Japan and the US, the increase in the domestic
energy content in total exports was primarily due to the rise in the
(corresponding domestic) energy inputs in manufacturing.

During the crisis period 2007-2009, following
the slump in global trade, the previous upward trend in the share of foreign
energy inputs in total energy embodied in manufacturing and service exports
ended or in some cases temporarily reversed. Panel B in Table 5.2 above showed
that in the period 2007-2009 the share of foreign-energy inputs in total energy
embodied in exports stabilised in the EU-15 and USA and decreased in Japan and
the EU-12. This may be due in part to the fact that manufacturing exports,
which were more severely hit overall during the crisis, account for a larger
share of total exports in Japan and in the EU-12.

Figure 3.10 – Energy (TJ, domestic and foreign)
content in (manufacturing, services and total) exports (Million USD, 1995,
2007)

Source: WIOD.

Figures 3.9 and 3.10 show for the period
1995-2007 an overall increase in the energy content in manufacturing (except in
the EU-12) and to a lower extent in service exports (except for the EU-12 and
EU-15). These figures also suggest that this could in part be related to the
increasing globalisation of production and the increasing weight of
foreign-energy inputs. Panel B in Table 3.2 points in the same direction by
showing a steady rise in the share of foreign-energy inputs in the total energy
embodied in exports (both in manufacturing and services up to 2007). Subsection
3.2.3 below presents a short exploratory analysis of the country and sectoral
trends in the energy content in exports in relation to globalisation of
production and trade.

Figure 3.11 further illustrates the geographic
patterns implicit in the changes in the structure of the energy inputs embodied
in exports over the period 1995-2007. The figure presents the changes in the
shares of energy inputs embodied in manufacturing, services and total exports for
each of the four economies (e.g. the share of domestic-energy inputs in total
energy embodied in the EU-15 exports of services decreased by 19% in the period
1995-2007, while the share of energy inputs that EU-15 exporters sourced
directly and indirectly from the BRII countries increased by 5% in the same
period).

Figure 3.11 shows a large shift overall from
domestic to foreign energy inputs embodied in exports in the period 1995-2007.
Interestingly, the figure also reveals for this period a higher (or at least
comparable in the case of Japan) shift towards foreign-energy inputs in service
exports relative to manufacturing exports. The exception is the EU-12, whose
share of domestic-energy inputs in manufacturing exports declined
(significantly by 22 %) by more than twice the contraction observed in the
share of domestic-energy inputs in service exports. A major and almost
equivalent drop (19%.) was observed in the share of domestic-energy inputs in
EU-15 exports of services. This, together with the relative weights of the
manufacturing and services in total exports in the EU-12 and EU-15, explains
why the European economies had the largest falls in the share of
domestic-energy inputs in total exports. The US had a much lower reduction in
the share of domestic-energy inputs in exports (around 4% in manufacturing and
6% in services).

The reciprocal increase in the share of
foreign-energy inputs embodied in exports was not distributed equally across
all trade partners. However, almost all of them increased their shares of total
energy inputs embodied in the exports in the period 1995-2007. The very few
exceptions concern Japan. There were marginal decreases in the shares of S. Korea and EU-15 energy inputs in Japanese service exports or in the share of US, Canadian
and EU-15 energy inputs in Japanese manufacturing exports. This means that in
the case of Japan domestic energy inputs, but also (to a minor extent) those
from some foreign countries, were shifted to other economies (e.g. China and the RoW).

Figure 3.11 – Changes in the share of energy inputs embodied in exports in the period
1995–2007 (in p.p.)

Source: WIOD.

Figure 3.12 below summarises the main changes in
the structure of (shares per trade partner in) foreign-energy inputs embodied
in exports. A joint reading of Figures 3.11 and 3.12 shows that in the period
1995-2007 a significant part of the energy inputs embodied in exports were
diverted from domestic to foreign countries, in particular to China.

Figure 3.12 shows that this is particularly
noticeable in manufacturing, where off-shoring trends in the period 1995-2007
led to virtually a doubling of the share (8 times higher in the case of EU-12)
of Chinese energy inputs in the foreign-energy inputs in manufacturing exports.
The increase in the weight of China as source of foreign-energy inputs led to
an overall contraction in the shares of other trade partners. Overall, the
shares of the RoW or the BRII contracted as well as the share of energy inputs
embodied in bilateral manufacturing trade between the EU-12, EU-15, Japan and the US.

Compared to manufacturing, the rise in the
weight of China as source of foreign-energy inputs embodied in service exports
was less pronounced, except for Japan. For Japan in the period 1995-2007, the
share of Chinese energy inputs in the foreign-energy inputs in Japanese service
exports also more than doubled, while the corresponding shares of S. Korea and EU-15 were roughly halved. In the EU-15, despite the significant decline in
the relative weight of domestic-energy inputs in service exports (remember
Figure 3.11), the relative increase in Chinese energy inputs was less
pronounced and the US and the EU-12 kept their shares broadly stable.
Similarly, in the US in the period 1995-2007, the shares of Canadian and EU-15
energy inputs in US service exports remained fairly stable while the increase
in the corresponding share of China was much smaller compared to manufacturing.

Regarding the recent crisis period, Figure 3.15 shows that China continued to increase its share of foreign-energy inputs in exports both for
manufacturing and services, now at the expense of the other trade partners in
general. Over the whole period (1995-2009), it more than doubled its share of
the foreign-energy inputs embodied in both manufacturing and service exports of
the EU-15, Japan and the US (the corresponding increase was much higher in the
case of the EU-12).

Figure 3.12 – Shares (per trade partner) in
foreign-energy inputs embodied in exports,
1995, 2007, 2009

Source: WIOD.

The changes in the sourcing structure of
foreign-energy inputs embodied in exports reflect many factors such as
differences in energy-efficiency trends across countries and sectors, together
with global-trade and vertical-specialisation developments. For instance, Figure 3.12 shows a relatively high share of the
EU-15 in the foreign-energy inputs embodied in EU-12 exports (for
manufacturing, services and total exports). This is to a great extent a
reflection of the strong links and importance of the EU-15 (e.g. as providers
of intermediate inputs) in the import content of EU-12 exports (documented in
Chapter 2). Subsection 3.2.4 below analyses in more detail the relations
between imports and foreign-energy content in exports and some of their
implications for competitiveness across countries and sectors.

              3.2.3. Globalisation and
the energy content in exports worldwide

This section explores to what extent
globalisation and increasing vertical specialisation have been followed by
changes (and eventually some convergence) in the energy content in exports at the
world level. World exports are proxied by the whole WIOD exports. The different
developments and contributions of manufacturing and service exports are also
briefly analysed, focusing on the long term changes in the period 1995-2007.

Figure 3.13 plots the changes (in the period
1995-2007) against the level of the energy content in total exports in 2007.
The size of the bubbles reflects the proportion that the energy embodied in
each of the ten economies’ total exports makes up of the total energy embodied
in (the whole ten economies’) WIOD total exports. The world is proxied by total
WIOD and is represented by the largest circle (with vertical and horizontal
lines crossing at its centre).

Figure 3.13 – Energy content in total exports:
change 1995-2007 versus level in 2007

Source: WIOD. Note: The size
of the bubbles reflects the weight that the energy embodied in the each economy's
total exports has in the total energy embodied in all WIOD total exports in
2007. Total WIOD is represented by the largest circle.

The figure shows an increase (of 8 %, see
Table 3.3) in the energy use per unit of worldwide exports in the period 1995-2007.
This was a period of sustained growth in global trade and intensified vertical
specialisation and appears to have led to significant reductions and some
convergence in the energy content in exports for economies such as the EU-12, China and the RoW.

China achieved partial convergence by reducing
the energy content in its exports by ¼ in the period 1995-2007 (see also Table
3.3 below). However, this reduction was much smaller than the increase (it
almost tripled) in China’s share in total WIOD exports in the same period. This
explains to a large extent the observed increase in energy inputs per unit of
worldwide exports in the period 1995-2007.[6] It has to be noted that
domestic-energy inputs account for a relatively high share (85 % in 2007)
of the energy content in Chinese exports. Even if the share of foreign-energy
inputs embodied in Chinese total exports has almost doubled (it increased from
8 % to 15 %) in the period 1995-2007, this is still a relatively low
value. In fact, this is the second-lowest value after the BRII economies and
less than half of the weight of foreign-energy inputs in exports in the
majority of the other economies (except for the US, Canada and the RoW, that
are less dependent on energy imports, see the last three columns in Table 3.3).

The increasing contribution and role of energy
embodied in Chinese exports can also be seen by comparing the shares in total
WIOD energy embodied with the shares in total exports in Table 3.3. Despite
some improvement, in 2007 China still had the second-highest ratio (after the
BRII economies) between the share of energy embodied and the share in total
WIOD exports (e.g. in 2007 China and the US already had comparable shares of
total WIOD exports – 11 % and 13 % respectively – while the share in
terms of energy embodied is considerably higher in China  – 17 %, as
against 10 % in the US).

BRII economies as a whole also contributed (but
to a lower extent than China) to the observed increase in energy inputs per
unit of total WIOD exports in the period 1995-2007. This is due to the marginal
increase in the BRII economies’ share of total WIOD exports, combined with
their overall high (unchanged) level of energy content in exports. The high
level of energy content in exports may in part reflect the relatively abundant
energy resources in some of the BRII economies.

The convergence (and significant reduction) in
the energy content in exports of the RoW economies was roughly proportional to
the increase in their share of total WIOD exports which led to a neutral
(slight reduction) effect on the energy inputs per unit of worldwide exports.

The EU-12 in particular (but also the EU-15)
outperformed overall in the reduction on energy content in exports. The EU-12
achieved full convergence with the total WIOD level in the period 1995-2007.
The increase in the energy inputs per unit of exports in South Korea and Japan may partly reflect the particular and intense vertical-specialisation links of
these two economies with China.

Figure 3.14 plots the changes (in the period
1995-2007) against the level of the energy content in manufacturing exports in
2007. The two panels are equal except for the size of the bubbles. In panel A
(on the left), the size of the bubbles reflects for each economy the weight that
the energy embodied in its manufacturing exports has in the energy embodied in
total WIOD manufacturing exports. On the right in panel B, the size of the
circles reflects the share of manufacturing exports in total WIOD manufacturing
exports in 2007. Total WIOD is represented by the largest circle in both
panels.

Table 3.3 – Energy
embodied (TJ) per unit of exports (USD million) and share of trade, energy and
foreign energy embodied in manufacturing, service and total exports: 1995,
1997, 2009

|| Energy (TJ) per unit of exports (Million USD) || Share in total WIOD exports || Share in total WIOD energy embodied || Share of foreign energy inputs

|| 1995 || 2007 || 2009 || 1995 || 2007 || 2009 || 1995 || 2007 || 2009 || 1995 || 2007 || 2009

MANUFACTURING (NACE D)

BRII || 74.9 || 82.4 || 77.3 || 5% || 6% || 5% || 11% || 13% || 12% || 7% || 7% || 7%

Canada || 32.8 || 37.6 || 34.8 || 6% || 4% || 3% || 6% || 4% || 3% || 22% || 26% || 24%

China || 68.1 || 51.2 || 46.1 || 5% || 15% || 21% || 10% || 21% || 28% || 8% || 15% || 17%

EU-12 || 63.6 || 30.0 || 27.3 || 3% || 5% || 5% || 5% || 4% || 4% || 14% || 36% || 33%

EU-15 || 17.6 || 20.5 || 17.8 || 27% || 24% || 23% || 14% || 14% || 12% || 23% || 34% || 35%

Japan || 11.1 || 19.5 || 20.1 || 14% || 8% || 7% || 5% || 4% || 4% || 29% || 38% || 34%

S. Korea || 33.4 || 48.8 || 50.0 || 4% || 5% || 5% || 4% || 6% || 7% || 30% || 31% || 32%

Mexico || 26.4 || 30.5 || 32.8 || 2% || 2% || 2% || 2% || 2% || 2% || 29% || 36% || 32%

USA || 25.9 || 31.8 || 28.6 || 17% || 12% || 11% || 14% || 10% || 9% || 16% || 20% || 20%

RoW || 53.8 || 37.6 || 37.3 || 18% || 20% || 17% || 30% || 21% || 19% || 12% || 33% || 31%

WIOD || 32.6 || 35.8 || 34.6 || 100% || 100% || 100% || 100% || 100% || 100% || - || - || -

SERVICES (NACE 50 to P)

BRII || 37.8 || 37.9 || 37.4 || 6% || 9% || 8% || 13% || 19% || 16% || 6% || 6% || 6%

Canada || 20.6 || 16.5 || 15.7 || 3% || 2% || 2% || 3% || 2% || 2% || 19% || 21% || 19%

China || 55.9 || 39.2 || 36.9 || 2% || 7% || 14% || 7% || 16% || 30% || 8% || 15% || 16%

EU-12 || 31.4 || 22.0 || 20.8 || 3% || 4% || 4% || 5% || 5% || 5% || 14% || 32% || 28%

EU-15 || 14.3 || 8.8 || 8.1 || 26% || 29% || 29% || 19% || 15% || 13% || 21% || 33% || 34%

Japan || 10.9 || 12.1 || 10.8 || 10% || 6% || 4% || 6% || 4% || 2% || 28% || 38% || 33%

S. Korea || 38.5 || 26.6 || 30.3 || 3% || 3% || 2% || 7% || 4% || 4% || 27% || 31% || 32%

Mexico || 16.2 || 17.1 || 17.1 || 2% || 2% || 1% || 2% || 2% || 1% || 25% || 31% || 28%

USA || 14.4 || 16.0 || 11.0 || 30% || 21% || 21% || 22% || 19% || 13% || 14% || 19% || 19%

RoW || 22.8 || 14.8 || 15.7 || 14% || 17% || 15% || 17% || 15% || 13% || 11% || 22% || 20%

WIOD || 19.2 || 17.5 || 17.6 || 100% || 100% || 100% || 100% || 100% || 100% || - || - || -

TOTAL EXPORTS (NACE A to P)

BRII || 62.2 || 64.7 || 61.0 || 6% || 7% || 7% || 12% || 14% || 13% || 6% || 6% || 6%

Canada || 32.0 || 34.1 || 31.4 || 5% || 4% || 3% || 6% || 4% || 3% || 19% || 21% || 19%

China || 66.6 || 49.7 || 44.5 || 4% || 11% || 17% || 9% || 17% || 24% || 8% || 15% || 16%

EU-12 || 55.5 || 29.6 || 27.6 || 3% || 4% || 4% || 5% || 4% || 4% || 14% || 32% || 28%

EU-15 || 17.0 || 17.4 || 14.9 || 25% || 23% || 22% || 14% || 12% || 10% || 21% || 33% || 34%

Japan || 11.0 || 17.8 || 18.8 || 12% || 6% || 6% || 4% || 4% || 3% || 28% || 38% || 33%

S. Korea || 34.2 || 45.3 || 46.9 || 4% || 4% || 4% || 4% || 5% || 5% || 27% || 31% || 32%

Mexico || 23.6 || 27.1 || 29.5 || 2% || 2% || 2% || 2% || 2% || 2% || 25% || 31% || 28%

USA || 22.2 || 26.1 || 21.8 || 19% || 13% || 13% || 14% || 10% || 9% || 14% || 19% || 19%

RoW || 45.0 || 35.9 || 36.5 || 21% || 25% || 22% || 31% || 27% || 26% || 11% || 22% || 20%

WIOD || 30.3 || 32.7 || 31.7 || 100% || 100% || 100% || 100% || 100% || 100% || - || - || -

Source: WIOD.

Manufacturing exports are dominant overall in
total exports (see Table 3.4 below) and appear to explain to a large extent the
observed increase in energy embodied in exports at world level in the period
1995-2007. Figure 3.12 shows (see also Table 3.3) an increase of 10 % in
the energy use per unit of world-wide manufacturing exports, which is slightly
higher than the (8 %) rise in energy use per unit of total exports
depicted in Figure 3.11 and Table 3.3 above.

The rise in energy content in total WIOD
manufacturing exports appears to be primarily driven by the increasing
vertical-specialisation links with China. The energy content in Chinese
manufacturing exports declined by ¼ in the period 1995-2007 while its share in
total WIOD manufacturing exports tripled in the same period (see Table 3.3). To
a lesser extent, the BRII economies as a whole and S. Korea also contributed to the rise in the energy use per unit of total WIOD manufacturing exports.
This can be seen by the position and size of bubbles in Figure 3.14. For China, BRII and S. Korea, the bubbles in panel B (reflecting export shares) are smaller relative to
panel A (in which they reflect the shares in energy embodied in exports).

Figure 3.14 – Energy content in manufacturing
exports: change 1995-2007 versus level in 2007

Source: WIOD. Note: In panel A
(on the left) the size of the bubbles reflects the weight that energy embodied
in the manufacturing exports of each economy has in the total energy embodied
in the whole WIOD manufacturing exports in 2007. On the right in panel B the
size of the bubbles reflects the share of manufacturing exports in total WIOD
manufacturing exports in 2007. Total WIOD is represented by the largest circle.

The EU-12 more than halved their energy inputs
per unit of manufacturing exports (starting from roughly the same level as China in 1995). The ROW economies also reduced significantly (by 30 %) the energy
content in exports and moved closer to the total WIOD average in the period
1995-2007.

Figure 3.15 presents similar plots of the
changes (in the period 1995-2007) against the level of the energy content in
service exports in 2007. Unlike manufacturing, the energy inputs embodied in
service exports declined by 9 % in the period 1995-2007. The energy
content in service exports is converging in the majority of countries, except
for the BRII economies, as with manufacturing. Despite a significant
improvement, in China the energy content in service exports in 2007 was similar
to the level in the BRII economies.

Services and manufacturing have different
weights in the various economies. Moreover, for some economies exports from
other sectors such as agriculture, forestry or mining are also significant
(e.g. in the RoW, BRII economies and Canada, exports other than manufacturing
and services accounted for between 1/5 and 1/3 of the total exports in 2007,
see Table 3.4).

Figure 3.15 – Energy
content in service exports: change 1995-2007 versus level in 2007

Source: WIOD. Note: On the
left panel (A) the size of the bubbles reflects the weight that energy embodied
in the service exports (NACE 50 to P) of each economy has in the total energy
embodied in the whole WIOD service exports in 2007. On the right panel the size
of the bubbles reflects the share of service exports in total WIOD service
exports in 2007. Total WIOD is represented by the largest circle.

Table 3.4 – Shares of manufacturing,
services and other exports in total exports,

1995, 1997, 2009

|| MANUFACTURING (NACE D) || SERVICES (NACE 50 to P) || OTHER (NACE A to C, E,F)

|| 1995 || 2007 || 2009 || 1995 || 2007 || 2009 || 1995 || 2007 || 2009

BRII || 58% || 51% || 50% || 23% || 26% || 26% || 19% || 23% || 24%

Canada || 75% || 65% || 59% || 12% || 14% || 17% || 13% || 22% || 25%

China || 81% || 84% || 79% || 12% || 14% || 19% || 7% || 2% || 2%

EU-12 || 66% || 75% || 71% || 24% || 20% || 23% || 10% || 5% || 6%

EU-15 || 75% || 70% || 67% || 21% || 28% || 30% || 4% || 3% || 3%

Japan || 83% || 79% || 85% || 17% || 21% || 14% || 0% || 0% || 1%

S. Korea || 81% || 84% || 84% || 18% || 16% || 16% || 1% || 0% || 0%

Mexico || 68% || 69% || 72% || 21% || 15% || 14% || 12% || 16% || 14%

USA || 63% || 60% || 58% || 32% || 36% || 38% || 5% || 4% || 4%

RoW || 60% || 52% || 50% || 14% || 15% || 15% || 25% || 32% || 35%

WIOD || 70% || 66% || 64% || 21% || 22% || 23% || 10% || 12% || 12%

Source:WIOD.
3.2.4.
Foreign-energy inputs vs import content in exports

Figure 3.16 presents the shares (per
trade partner) of foreign-energy inputs and import content in exports (the
latter studied in Chapter 2 of this report) side-by-side. As expected, the figure depicts a significant overall similarity
between the two structures but also some important differences. Firstly, energy-rich
economies (such as some countries in BRII and ROW) have a higher weight in
terms of foreign-energy inputs relative to import contents in total exports.
This general pattern is also found for manufacturing and service exports. The
direction of the changes (in the period 1995-2007) in the shares of foreign
energy sourced from these (BRII and ROW) countries tend to follow the direction
of the changes in import content in exports. However, the relationship is not
one-to-one: the ratio between the shares in foreign energy and import content
in exports is rising overall for the BRII and declining for the RoW (see Table
3.5 below), perhaps reflecting many factors such as energy-efficiency trends,
preferential trade and energy supply relations between different countries,
etc.

Figure 3.16 – Shares (per trade partner) in
foreign energy inputs vs import content in
EU-12, EU-15, Japan, US, China, BRII and RoW total exports, 1995, 2007

Source: WIOD.

Secondly, advanced economies (in particular the
EU-15, Japan and to a lesser extent the US) tend to have higher shares of
import content relative to foreign-energy content in exports. Both shares
decreased overall for the EU-15, Japan and the US in the period 1995-2007. Thirdly,
and unlike these advanced economies, China significantly increased its overall
share of both foreign-energy inputs and import content in exports over the same
period. However, China’s share of foreign-energy inputs is higher (or broadly
as great in some cases in 2007) than the share of import content in exports.
Fourthly, regarding China’s exports, the increase in energy use was reflected
in a significant increase in the energy content share of the BRII in the period
1995-2007, mostly at the expense of the RoW economies. These movements do not
have an immediate parallel in the import-content structure of Chinese exports.
In fact, partly reflecting the increased use of non-energy raw material inputs,
the import-content share of the RoW economies increased over this period,
mostly at the expense of Japan and to a much lesser extent of the other
economies (in 2007, the EU-15 as a whole had the second-largest import-content
share in Chinese exports, after the ROW). The figures for manufacturing and
service exports show similar patterns and were omitted.

Table 3.5 presents the ratio between the shares
in foreign-energy inputs and import content in manufacturing, service and total
exports (panels A, B, C respectively) for all ten economies. The ratio provides
a measure of relative energy intensity in total foreign inputs. It can
similarly be seen as the share of energy in total (energy and non-energy)
inputs sourced from a given trade partner relative to the corresponding average
share for all trade partners of a given country. Therefore it indicates (in
relative terms, per trade partner) how energy intensive the import contents are
in the exports of a given country. A value lower than one indicates that a
given trade partner has a lower than average weight of energy inputs relative
to all foreign inputs embodied in the exports of a given country. In order to
facilitate reading, values lower or equal to one (and higher than ½) are
highlighted in yellow. Values lower or equal to ½ are highlighted in green.

The import content of exports is growing with
the globalisation of production and vertical specialisation and this ratio
provides a summary of the relative energy intensities and vulnerabilities to
increases in the relative price of energy. It permits analysis of relative
performances across countries and sectors as a consequence, for instance, of
specialisation or energy-efficiency trends. For instance, the two columns for China indicate (for the years 1995 and 2007) the ratio between foreign-energy inputs and
import contents in Chinese (manufacturing, service and total) exports. In 2007,
the Japanese share of total foreign-energy inputs embodied in Chinese exports
was only half of the Japanese share in the import content of Chinese exports.
For the EU-15, the corresponding figure was even smaller. Incidentally, in this
particular case the ratios for Chinese total exports and manufacturing exports
are identical (in terms of the figures presented, rounded to one decimal
place). For Chinese service exports in 2007, the lead of the EU-15 in terms of
the lowest relative weight of energy inputs is even more pronounced.

The diagonal is empty because only
foreign-energy inputs and import content in exports are being compared. The
last two columns (labelled WIOD) present the ratio between the shares in
foreign-energy inputs and import content in total WIOD exports (for
manufacturing, service and total exports). Standard deviations are presented in
the last three rows for manufacturing, service and total exports.

The EU-15 and Japan have the lowest relative
weight of energy inputs in the total foreign inputs incorporated in exports
(globally and overall across countries and sectors, manufacturing and
services). Among the economies with a high overall dependency on energy
imports, the EU-15 as a whole and Japan are therefore those economies that in
principle will suffer lower external competitiveness losses as a result of an
increase in the relative price of energy. One distinction is that the EU-15
slightly reduced overall the relative weight of energy inputs in total inputs
across countries and sectors in the period 1995-2007 (one exception was the
increase from 1.4 to 1.7 in the relative weight of EU-15 energy inputs embodied
in US service exports).

By contrast, for Japan the relative weight of
energy inputs in the total inputs it embodies in exports increased overall in
the same period. The EU-15 and Japan are among the countries having the lowest
dispersion in the relative weights of energy inputs, reflecting a relatively
diversified sourcing among their trade partners of the energy inputs embodied
in their exports.

In the US, the relative weight of energy inputs
is higher (twice the relative weight in the EU-15 and Japan in 2007 in WIOD exports) and, as with Japan, also increased overall in the period. Despite
this increase, the relative weight of US energy inputs is overall below (or in
some cases close to) the average. The standard deviation of the relative weight
of energy inputs embodied in US exports decreased, particularly in
manufacturing exports.

The EU-12 as a whole achieved the greatest
reduction in the relative weight of energy inputs embodied in exports (halving
or more than halving the ratio for all WIOD service, manufacturing and total
exports) in the period 1995-2007. In 2007, the relative weight of EU-12 energy
inputs embodied in exports was already below the average for total WIOD and for
many of the single-country exports. The standard deviation of the relative
weights of foreign inputs embodied in EU-12 exports increased, in particular
for manufacturing, as result of the increase in the relative weight of the
energy inputs sourced from the BRII in the period 1995-2007.

Table
3.5 – Ratio
between the shares in foreign energy inputs and import content in
manufacturing, service and total exports in 1995 and 2007

|| BRII || Canada || China || EU-12 || EU-15 || Japan || Korea || Mexico || USA || RoW || WIOD

|| 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007 || 1995 || 2007

A) Manufacturing exports

BRII || || || 3.4 || 2.7 || 2.3 || 2.9 || 2.4 || 3,. || 1.7 || 2.0 || 2.2 || 2.3 || 2.5 || 2.7 || 3.6 || 2.7 || 2.7 || 2.5 || 3.1 || 2.6 || 2.5 || 2.6

Canada || 1.2 || 1.3 || || || 1.3 || 1.4 || 1.4 || 1.6 || 1.1 || 1.1 || 1.0 || 0.9 || 1.3 || 1.1 || 1.7 || 1.1 || 1.3 || 1.1 || 2.0 || 1.4 || 1.3 || 1.2

China || 1.4 || 1.3 || 2.8 || 1.5 || || || 1.9 || 1.5 || 1.4 || 1.0 || 1.6 || 1.0 || 1.5 || 1.2 || 2.7 || 1.4 || 2.1 || 1.2 || 2.5 || 1.2 || 2.0 || 1.2

EU-12 || 1.8 || 1.0 || 2.8 || 1.2 || 2.5 || 1.0 || || || 1.5 || 0.7 || 1.8 || 0.7 || 2.0 || 0.7 || 3.2 || 1.0 || 2.3 || 1.0 || 3.1 || 1.1 || 2.2 || 0.9

EU-15 || 0.4 || 0.4 || 0.6 || 0.5 || 0.4 || 0.4 || 0.5 || 0.5 || || || 0.3 || 0.3 || 0.4 || 0.3 || 0.6 || 0.5 || 0.5 || 0.5 || 0.7 || 0.4 || 0.5 || 0.4

Japan || 0.3 || 0.4 || 0.3 || 0.3 || 0.3 || 0.5 || 0.3 || 0.4 || 0.2 || 0.3 || || || 0.3 || 0.4 || 0.3 || 0.4 || 0.3 || 0.4 || 0.4 || 0.4 || 0.3 || 0.4

Korea || 0.7 || 1.2 || 1.0 || 0.9 || 1.0 || 1.3 || 0.6 || 0.9 || 0.5 || 0.7 || 0.9 || 0.9 || || || 0,8 || 0.8 || 0.6 || 1.0 || 1.1 || 1.3 || 0.8 || 1.1

Mexico || 0.9 || 1.0 || 0.8 || 0.7 || 1.1 || 0.8 || 1.2 || 1.1 || 0.6 || 0.7 || 0.7 || 0.6 || 1.0 || 0.7 || || || 0.8 || 0.8 || 1.2 || 0.8 || 0.8 || 0.8

USA || 0.8 || 0.8 || 0.9 || 0.8 || 0.8 || 0.8 || 0.7 || 1.0 || 0.5 || 0.6 || 0.5 || 0.6 || 0.7 || 0.6 || 0.9 || 0.9 || || || 0.9 || 0.8 || 0,7 || 0.8

RoW || 1.9 || 1.4 || 2.2 || 1.6 || 1.7 || 1.2 || 2.0 || 1.5 || 1.2 || 1.1 || 1.3 || 1.2 || 1.8 || 1.3 || 2.0 || 1.1 || 1.5 || 1.2 || || || 1.8 || 1.3

St dev || 0.6 || 0.4 || 1.2 || 0.7 || 0.8 || 0.7 || 0.8 || 0.9 || 0.5 || 0.5 || 0.6 || 0.6 || 0.7 || 0.7 || 1.2 || 0.7 || 0.9 || 0.6 || 1.0 || 0.7 || 0.8 || 0.6

B) Service exports

BRII || || || 2.7 || 2.1 || 1.9 || 2.4 || 2.5 || 2.8 || 1.7 || 2.1 || 1.7 || 2.4 || 1.9 || 2.7 || 2.5 || 2.0 || 2.7 || 2.2 || 3.2 || 3.2 || 2.2 || 2.5

Canada || 0.8 || 1.1 || || || 0.9 || 1.1 || 1.2 || 1.1 || 1.0 || 1.0 || 0.7 || 0.9 || 0.9 || 1.2 || 1.3 || 0.8 || 1.5 || 1.6 || 2.1 || 1, || 1.3 || 1.5

China || 1.0 || 1.2 || 2.0 || 1.1 || || || 1.6 || 1.2 || 1.2 || 1.0 || 1.1 || 1.1 || 0.8 || 1.3 || 2.1 || 0.9 || 1.6 || 0.8 || 2.6 || 1.3 || 1.5 || 1.1

EU-12 || 1.3 || 0.8 || 1.6 || 1.0 || 1.7 || 0.8 || || || 1.4 || 0.7 || 1.0 || 0.6 || 0.8 || 0.6 || 2.1 || 0.7 || 1.2 || 0.7 || 2.9 || 1.0 || 1.6 || 0.8

EU-15 || 0.3 || 0.3 || 0.5 || 0.6 || 0.4 || 0.3 || 0.6 || 0.6 || || || 0.4 || 0.3 || 0.3 || 0.3 || 0.4 || 0.7 || 0.4 || 0.7 || 0.8 || 0.5 || 0.5 || 0.4

Japan || 0.2 || 0.3 || 0.3 || 0.5 || 0.3 || 0.5 || 0.2 || 0.3 || 0.2 || 0.3 || || || 0.4 || 0.4 || 0.3 || 0.4 || 0.2 || 0.4 || 0.4 || 0,5 || 0.3 || 0.4

Korea || 0.7 || 1.5 || 1.1 || 1.0 || 1.7 || 1.9 || 0.4 || 0.8 || 0.7 || 0.7 || 1.4 || 0.9 || || || 0.7 || 0.9 || 0.8 || 1.7 || 1.4 || 1.8 || 1.3 || 1.4

Mexico || 0.7 || 1.0 || 0.8 || 0.9 || 0.9 || 0.7 || 1.1 || 1.0 || 0.8 || 0.9 || 1.0 || 1.0 || 1.1 || 1.1 || || || 0.9 || 1.2 || 1.3 || 1.0 || 0.9 || 1.2

USA || 0.6 || 0.9 || 1.0 || 1.0 || 0.7 || 0.7 || 0.6 || 0.9 || 0.5 || 0.7 || 0.6 || 0.9 || 0.6 || 0.9 || 1.1 || 1.2 || || || 1.0 || 1.0 || 0.7 || 0.8

RoW || 2.2 || 1.5 || 1.6 || 1.2 || 1.8 || 1.3 || 2.0 || 1.3 || 1.4 || 1.1 || 1.3 || 1.1 || 1.7 || 1.4 || 1.7 || 1.0 || 1.5 || 1.0 || || || 1.9 || 1.2

St dev || 0.6 || 0.4 || 0.8 || 0.5 || 0.7 || 0.7 || 0.8 || 0.7 || 0.5 || 0.5 || 0.4 || 0.6 || 0.5 || 0.7 || 0.8 || 0.4 || 0.7 || 0.6 || 1.0 || 0.8 || 0.6 || 0.6

C) Total exports

BRII || || || 3.4 || 2.7 || 2.3 || 2.8 || 2.4 || 3.4 || 1.7 || 2.0 || 2.1 || 2.3 || 2.4 || 2.7 || 3.5 || 2.6 || 2.7 || 2.4 || 3.1 || 2.6 || 2.5 || 2.5

Canada || 1.1 || 1.2 || || || 1.3 || 1.4 || 1.3 || 1.5 || 1.0 || 1.1 || 1.0 || 0.9 || 1.2 || 1.1 || 1.6 || 1.1 || 1.3 || 1.2 || 2.0 || 1.4 || 1.3 || 1.2

China || 1.3 || 1.3 || 2.8 || 1.4 || || || 1.8 || 1.4 || 1.4 || 1.0 || 1.5 || 1.0 || 1.4 || 1.2 || 2.6 || 1.3 || 2,0 || 1.1 || 2.5 || 1.2 || 1.9 || 1.2

EU-12 || 1.7 || 0.9 || 2.7 || 1.2 || 2.4 || 1.0 || || || 1.5 || 0.7 || 0.7 || 0.7 || 1.9 || 0.7 || 3.0 || 1.0 || 2.1 || 0.9 || 3.0 || 1.1 || 2.2 || 0.9

EU-15 || 0.4 || 0.4 || 0.6 || 0.5 || 0.4 || 0.4 || 0.5 || 0.5 || || || 0.4 || 0,3 || 0.4 || 0.3 || 0.6 || 0.5 || 0.5 || 0.5 || 0.7 || 0.4 || 0.5 || 0.4

Japan || 0,3 || 0.4 || 0.3 || 0.4 || 0.3 || 0.5 || 0.3 || 0.4 || 0.2 || 0.3 || || || 0.3 || 0,4 || 0.3 || 0.4 || 0.3 || 0.4 || 0.4 || 0.4 || 0.3 || 0.4

Korea || 0.7 || 1.3 || 1.0 || 0.9 || 1.1 || 1.3 || 0.5 || 0.8 || 0.5 || 0.7 || 1.0 || 0.9 || || || 0.8 || 0.8 || 0.6 || 1.1 || 1.1 || 1.3 || 0.9 || 1.1

Mexico || 0.9 || 1.0 || 0.8 || 0.7 || 1.0 || 0.8 || 1.2 || 1.1 || 0.7 || 0.7 || 0.7 || 0.6 || 1.0 || 0.7 || || || 0.8 || 0.9 || 1.2 || 0.8 || 0.8 || 0.8

USA || 0.7 || 0.9 || 0.9 || 0.8 || 0.8 || 0.8 || 0.7 || 1.0 || 0.5 || 0.7 || 0.5 || 0.7 || 0.7 || 0.7 || 0.9 || 1.0 || || || 0.9 || 0.8 || 0.7 || 0.8

RoW || 1.9 || 1.4 || 2.2 || 1.6 || 1.7 || 1.2 || 2.0 || 1.5 || 1.3 || 1.1 || 1.3 || 1.2 || 1.8 || 1.3 || 2.0 || 1.1 || 1.5 || 1.2 || || || 1.8 || 1.3

St dev || 0.6 || 0.4 || 1.1 || 0.7 || 0.7 || 0.7 || 0.8 || 0.9 || 0.5 || 0.5 || 0.6 || 0.6 || 0.7 || 0.7 || 1.1 || 0.6 || 0.8 || 0.6 || 1.0 || 0.7 || 0.8 || 0.6

.

Source: WIOD. Note: values
lower or equal to one and higher than ½ are highlighted in yellow. Values lower
or equal to ½ are highlighted in green.

China and the RoW economies have also
significantly reduced the relative weight of their energy inputs embodied in
the exports of the other countries. However, unlike the EU-12 the relative
weight of Chinese and RoW energy inputs in general remain above the average of
relative weight of foreign energy inputs embodied in the exports of most of the
countries in 2007. Exceptions include the considerable convergence of China towards the average of the relative weights in energy inputs embodied in EU-15 and
Japanese manufacturing and total exports.

Some of the BRII countries are energy-rich and
this may in part explain why energy has a relatively high weight in the BRII
inputs embodied in exports of the other economies. The relative weight of BRII
energy inputs in manufacturing and service exports has increased in the period
1995-2007.

Table 3.5 (panel C) indicates a constant or
reduced variability of the relative weight of energy in the total foreign
inputs embodied in the total exports of countries and total WIOD exports in the
period 1995-2007 (the exception is the EU-12). This appears to be result of the
convergence that occurred across countries in terms of the weight of energy
inputs embodied in manufacturing exports (as indicated by overall lower  –
except for the EU-12 – standard deviations in 2007 in panel A of Table 3.5).

              3.2.5 Domestic-energy inputs
vs domestic inputs in exports

Figure 3.17 presents the
country shares
in total (the across-countries sum of) domestic energy inputs in exports
side-by-side with the shares in total (the sum of) domestic inputs in exports
(the latter studied in Chapter 2 of this report).

Figure 3.17 – Shares in
domestic energy inputs vs. domestic content
in (manufacturing, service and total) exports, 1995, 2007 and 2009

Source: WIOD.

Figures 3.16 and 3.17
depict broadly similar patterns. The BRII economies as a whole have
relatively high energy intensities in total domestic inputs embodied in
exports. By contrast, in the EU-15, Japan and (to a lesser extent) the US, the share in domestic content in exports is higher than the share in domestic-energy
inputs in exports. However, both shares are decreasing over time, in particular
in the US and Japan (including during the crisis period 2007-2009). They are
giving way to the larger shares of China in both domestic-energy inputs and
domestic content in exports (as in the case described above of the
foreign-energy inputs and import content in exports), reflecting the Chinese
exports boom in the period.

Table 3.6 presents the ratio between the shares
in domestic energy inputs and domestic content (in manufacturing, service and
total) exports. Similarly, the ratio provides a measure of energy intensity
relative to total domestic inputs embodied in exports. Again, a value lower
than one indicates that a given country has a lower than average weight of
energy inputs relative to all domestic inputs embodied in exports (which for economies
that are dependent on energy imports may represent relatively lower potential
competitiveness losses arising from an increase in the relative price of
energy).

Table
3.6 – Ratio
between the shares in domestic energy inputs and domestic content in manufacturing,
service and total exports in 1995, 2007 and 2009

|| Manufacturing || Services || Total exports

|| 1995 || 2007 || 2009 || 1995 || 2007 || 2009 || 1995 || 2007 || 2009

BRII || 2.6 || 2.9 || 2.8 || 2.2 || 2.6 || 2.5 || 2.4 || 2.5 || 2.4

Canada || 1.2 || 1.3 || 1.2 || 1.2 || 1.0 || 1.0 || 1.2 || 1.3 || 1.2

China || 1.9 || 1.2 || 1.1 || 2.5 || 2.0 || 1.9 || 1.9 || 1.2 || 1.1

EU-12 || 2.2 || 0.9 || 0.8 || 1.5 || 1.2 || 1.2 || 1.9 || 0.9 || 0.9

EU-15 || 0.5 || 0.5 || 0.4 || 0.7 || 0.4 || 0.4 || 0.5 || 0.4 || 0.4

Japan || 0.3 || 0.4 || 0.5 || 0.5 || 0.6 || 0.5 || 0.3 || 0.4 || 0.4

Korea || 0.8 || 1.2 || 1.3 || 2.0 || 1.3 || 1.5 || 0.9 || 1.2 || 1.2

Mexico || 0.8 || 1.0 || 1.1 || 1.0 || 1.3 || 1.3 || 0.8 || 0.9 || 1.1

USA || 0.8 || 1.0 || 1.0 || 0.8 || 1.0 || 0.7 || 0.7 || 0.9 || 0.8

RoW || 1.9 || 1.1 || 1.2 || 1.2 || 0.8 || 0.9 || 1.7 || 1.3 || 1.3

St dev || 0.8 || 0.7 || 0.6 || 0.7 || 0.7 || 0.7 || 0.7 || 0.6 || 0.6

Source: WIOD. Note: values
lower or equal to one and higher than ½ are highlighted in yellow. Values lower
or equal to ½ are highlighted in green.

The EU-15 and Japan also have the lowest
relative energy intensity in terms of domestic inputs embodied in (total,
manufacturing and service) exports. The energy intensity ratio decreased by
almost ½ for the EU-15 in the period 1995-2007, eliminating the gap with
manufacturing and broadly converging to the Japanese energy-intensity levels
(that increased slightly over the period). The US also has a higher energy
intensity when it comes to domestic inputs in exports (that, as in Japan, increased slightly in the period 1995-2007), but that still remains below the
average overall (for manufacturing, service and total exports). For these
economies, the energy intensity levels in the domestic and foreign content in
exports (the latter presented in Table 3.5) are broadly similar.

The EU-12 significantly reduced energy intensity
in domestic inputs in manufacturing exports but achieved only a much smaller
reduction in relation to service exports. The weight of energy inputs in
domestic inputs embodied in service exports remained above one over the whole
period and the gap vis-à-vis the EU-15 was not reduced. This may be one of the
factors undermining the competitiveness of service exports in the EU-12 and may
partly explain its lower growth when compared to manufacturing exports in the
period (see Figure 3.9 and Table 3.3 for the evolution of the EU-12 market
shares in each sector relative to total WIOD exports). The contrast is evident
not only with the substantial reduction in the weight of energy inputs in the
domestic content in manufacturing exports, but also with the roughly similarly
reduction observed in Table 3.5 above in terms of the relative weight of the
EU-12 energy inputs embodied in both manufacturing and service exports of the
other economies.

Similarly, China has considerably reduced the
energy intensity of the domestic content in manufacturing exports but to a much
lesser extent in service exports. This contrasts with the RoW, where the weight
of energy in the domestic content in exports declined both in manufacturing and
services.

The standard deviations at the bottom of Table
3.6 point to some convergence in the energy intensity of domestic inputs
embodied in manufacturing but not in service exports. This may be partly
explained by an overall greater competition, larger weight of tradable goods
and more developed vertical specialisation within manufacturing. Table 3.5
indicated some convergence in the energy intensity of foreign energy inputs in
the import content of both manufacturing and service exports. This is a further
indication of the importance of internationalisation and the development of
cross-border production networks for the reduction and convergence of
energy-intensity levels across countries. The next subsection, focusing on manufacturing,
analyses whether part of the reduction of the energy intensity of the inputs
embodied in exports is due to improvements in energy efficiency.

              3.2.6 Measuring energy
efficiency in the manufacturing sector

There
has been a substantial improvement in industrial competitiveness due to
investment in more energy-efficient technology and innovative products and
processes. This subsection analyses how to measure energy-efficiency changes
that are genuinely the result of technology improvements in EU manufacturing
and to what extent they have contributed to improved competitiveness.

Energy
efficiency is analysed by breaking down the changes in energy use to a number
of causative factors, focusing on manufacturing in the European Union and on
its major competitors.

Table
3.7 presents energy intensity in the EU-27 in the years 1995, 2007 and 2009.
Manufacturing activities involve transforming different material inputs into
products and tend to use relatively more energy in terms of gross output
volumes but not in relation to value added. Manufacturing sectors contributed
significantly to the overall improvement in energy productivity in the period
1995-2009. The improvement was particularly noticeable in energy intensive
sectors such as Coke, Refined Petroleum and Nuclear Fuel, Basic Metals and
Fabricated Metal or Chemicals, but also in some less energy-intensive sectors.
The few exceptions, such as Wood and Products of Wood and Cork, seem to be more
a result of a cyclical increase in measured energy intensity that may be due to
the crisis and to low capacity utilisation.

Table 3.7 Energy intensity in TJ per Unit of Output (O) and
Value Added (VA) (EU-27 in 1995 prices and US Dollars)

NACE Rev. 1.1 || Description || Energy Intensity || Change

1995 || 2007 || 2009 || 1995-2009

O || VA || O || VA || O || VA || O || VA

TOTAL || ALL SECTORS || 5.94 || 31.63 || 4.48 || 22.90 || 4.37 || 23.98 || -26% || -24%

D || MANUFACTURING (Total) || 10.28 || 11.85 || 6.96 || 9.60 || 7.12 || 9.19 || -31% || -22%

15t16 || Food , Beverages and Tobacco || 1.97 || 7.84 || 1.48 || 6.15 || 1.47 || 6.33 || -25% || -19%

17t18 || Textiles and Textile || 2.13 || 6.31 || 1.49 || 4.66 || 1.35 || 4.19 || -36% || -34%

19 || Leather, Leather and Footwear || 1.24 || 4.31 || 0.81 || 3.06 || 0.77 || 2.79 || -38% || -35%

20 || Wood and Products of Wood and Cork || 2.79 || 8.21 || 2.84 || 9.41 || 3.42 || 11.31 || 23% || 38%

21t22 || Pulp, Paper, Printing and Publishing || 3.69 || 9.73 || 3.64 || 10.43 || 3.64 || 10.37 || -1% || 7%

23 || Coke, Refined Petroleum and Nuclear Fuel || 195.71 || 1231.89 || 128.76 || 1199.02 || 95.33 || 967.93 || -51% || -21%

24 || Chemicals and Chemical || 13.60 || 39.97 || 9.29 || 28.25 || 8.95 || 27.11 || -34% || -32%

25 || Rubber and Plastics || 1.62 || 4.40 || 1.47 || 4.36 || 1.41 || 4.23 || -13% || -4%

26 || Other Non-Metallic Mineral || 9.45 || 23.20 || 7.63 || 20.22 || 7.85 || 20.61 || -17% || -11%

27t28 || Basic Metals and Fabricated Metal || 7.83 || 22.46 || 5.24 || 16.38 || 4.70 || 15.11 || -40% || -33%

29 || Machinery, Nec || 0.95 || 2.54 || 0.57 || 1.73 || 0.61 || 1.82 || -36% || -28%

30t33 || Electrical and Optical Equipment || 0.68 || 1.92 || 0.33 || 0.87 || 0.31 || 0.84 || -54% || -56%

34t35 || Transport Equipment || 0.77 || 2.83 || 0.43 || 1.90 || 0.47 || 2.13 || -38% || -25%

36t37 || Manufacturing Nec; Recycling || 1.11 || 3.09 || 1.02 || 3.31 || 1.22 || 3.83 || 10% || 24%

Source:
WIOD.

The analysis of the changes in energy use and
the improvements in energy efficiency are carried out through a standard index
decomposition method (the Log-Mean Divisia Index, see Annex 1). The change in
total energy use in manufacturing sectors is decomposed into three factors: i)
scale; ii) composition and, most importantly, iii) ‘technical effect’. The
scale factor accounts for the change in energy use that is due to a change in
economic activity (overall level of production[7]). The composition
factor isolates the effect of sub-sectoral/structural changes within
manufacturing. Finally, the technical effect shows how energy use would have
changed if the total level of production (scale) and the industry structure
(composition) had remained unchanged over time.

Figure
3.18 presents the results of the decomposition for the EU, EU-15 and EU-12. The
grey lines in the figure show the development of total energy use in
manufacturing in the EU-27, EU-15, and EU-12. In general, the EU-15 aggregate
accounts for a very high share of the EU-27’s overall economic activity and
energy use in manufacturing sectors (that is the reason why the lines
corresponding to these two aggregates appear superimposed). The yellow lines
(for the scale effect, controlling for a fixed technology and sector
composition) indicate a significant increase in total energy use up to 2008 (in
particular in the EU-12, almost a 200 % increase from 1995 to 2008).
However, this effect was more than compensated for by the improvement in energy
efficiency (accounted for by the green lines). The better performance of EU-12
(vis-à-vis the EU-15) indicates a genuine improvement in energy efficiency in
manufacturing and an important contribution to the overall performance and
catching-up (from their low initial efficiency levels as observed above in
Figure 3.2). Finally, the blue lines indicate negligible composition effects
for the EU-15. For the EU-12, the composition effect indicates a shift towards
less energy-intensive manufacturing subsectors.

Figure
3.19 shows that the manufacturing sector in the US has improved its energy
efficiency and contributed to the overall improvement in energy-use in that
country. However, the technical effect is much smaller than the one observed in
the European Union. The scale effect is positive but also smaller compared to
the EU (largely a result of the higher growth in manufacturing output in the EU
in the period 1995-2007, as afterwards the drop in activity was roughly similar
in both areas).

Figure 3.18 – Index
Decomposition Analysis of Total Energy Use in Manufacturing Sectors Using the
Log Mean Divisia Index: EU-27, EU-15, and EU-12

Source:WIOD.

Figure 3.19 - Index
Decomposition Analysis of Total Energy Use in Manufacturing Sectors Using the
Log Mean Divisia Index: United States

Source: WIOD.

Japan, one of world leaders in energy efficiency
in manufacturing (see European Competitiveness Report 2011, Chapter 5), has not
achieved an improvement of the kind seen in the EU and the US in this period
(in fact, the technical effect even displays a slight upward trend in the period
from 1998-2009, see Figure 3.20). The scale effect is relatively flat and the
slight reduction in total energy use observed in the later period in the figure
is due to a shift towards less energy-intensive manufacturing sectors.

Figure 3.20 - Index Decomposition
Analysis of Total Energy Use in Manufacturing Sectors Using the Log Mean
Divisia Index: Japan

Source: WIOD.

Figure 3.21 shows that for China the increase in economic activity in the manufacturing sector was the dominant factor (it
would have accounted for an overwhelming 600 % increase in energy use had
other factors remained unchanged in the period 1995-2009). At the same time,
there was a significant improvement in energy efficiency and a progressive
shift towards less intensive manufacturing sectors. As a result, total energy
use of the Chinese manufacturing sector more than doubled from 1995 until 2009.

Figure 3.21 - Index
Decomposition Analysis of Total Energy Use in Manufacturing Sectors Using the
Log Mean Divisia Index: China

Source: WIOD.

So far, the analysis suggests that EU
manufacturing sectors had a relatively good performance overall in improving
energy efficiency and contributed to the leading position and eco-performance
of the European Union as a whole. Figure 3.22 reports the changes in total
energy use and the three decomposition factors per Member State in the period
1995-2009.

Figure 3.22 -
Decomposition Analysis of Total Energy Use in Manufacturing Sectors

Source: WIOD.

Overall total energy use in the manufacturing
sectors decreased from 1995 until 2009 in most of the Member States (there are
only a few exceptions, e.g. Lithuania). Those countries with a high scale
effect (Ireland and a subset of the EU-12 countries) are at the same time those
countries that overall achieved the greatest improvement in energy efficiency
(technical effect). However, all Member States (except five, Lithuania, Hungary, Italy, Portugal and Denmark) have improved energy efficiency in manufacturing.
There was a shift towards less energy-intensive sectors in the EU-12 countries
with only a few exceptions (in particular Bulgaria). The composition effect is
heterogeneous across EU-15 countries (e.g. there is no discernible shift
towards less energy-intensive sectors as observed in Figure 3.20 above for
Japan).

              3.3. ECO-INNOVATION ADOPTION AND THE COMPETITIVENESS OF EU FIRMS

This section analyses
the evidence for the adoption and development of eco-innovations by EU firms,
focusing on energy-efficient process technologies and products. It is of particular
interest to study how the adoption of energy efficiency translates into the
performance and competitiveness of European firms.

This section is
organised as follows: i) it starts by presenting some background and a short
literature review; ii) the second part studies the reasons why firms introduce
energy-efficient technologies; iii) the third part analyses whether firms that
introduce new products on the market that allow their customers to save energy
have a higher success rate in terms of commercialisation of their product
innovations, compared to conventional product innovators. The section ends with
a brief analysis of the competitive position of EU firms in the growing
cross-border investments in clean, more energy-efficient and other technologies
related to the development of environmental goods and services. This assessment
paves the way for the in-depth analysis that follows in Chapter 4 on general
FDI flows and their impact on competitiveness.

              3.3.1. Background and literature review

Eco-innovation is any
form of innovation resulting in or aiming at significant and demonstrable
progress towards the goal of sustainable development, through reducing impacts
on the environment, enhancing resilience to environmental pressures, or
achieving a more efficient and responsible use of natural resources (European
Commission (2011)). It can be understood as the ﬁrst introduction of a
pollution-abatement technology or resource-saving technology (energy or
material inputs) by a ﬁrm. It is required that the respective technology
only to be novel to the introducing ﬁrm and, of course, does not
distinguish between technology invented by the ﬁrm itself and the
adoption of well-known abatement technology that had already been invented by
others (see Rennings (2000) for a more detailed discussion).

The choice to invent or
to adopt a new process technology is determined by several factors (such as
input prices or regulations), but eco-innovation has also associated a positive
environmental externality. While for conventional technical change the
innovator is rewarded with private benefits, the eco-innovator in general also
creates social benefits and has to bear the costs of introducing technical
change alone. For energy-efficiency technology, there are usually both private
returns (e.g. lower energy and maintenance costs, etc.) and social benefits
(such as reductions in CO2 emissions).

This chapter restricted
the scope of the empirical analysis to energy-saving technologies and the words
‘eco-innovation’, ‘invention’, ‘innovation’ and ‘adoption’ - of an existing
technology that is new to the firm - have been used interchangeably.

The Community Innovation
Survey 2008 (CIS 2008) reports information for more than 76500 firms across 18
EU Member States on whether they adopted energy-saving technologies (amongst
other eco-innovations) between 2006 and 2008[8]. The countries
included are Bulgaria, Cyprus, the Czech Republic, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Lithuania, Latvia, Malta, The Netherlands, Portugal, Romania, Slovakia, and Sweden.

A first look at both the CIS micro-data and WIOD
sectoral data (see Figure 3.23) reveals that manufacturing – as a whole and in
particular energy-intensive sectors – achieved a relatively greater reduction
in their energy intensity and that this corresponds to higher eco-innovation
activities observed in the firm-level data for the same sectors. The left-hand
side of Figure 3.23 presents the change in energy intensity from 1995 until
2009, based on WIOD. The share of firms in the CIS micro-data that introduced
energy-saving process technologies between 2006 and 2008 is presented in the
right-hand-side (RHS) figure.

Figure 3.23 -
Change in Energy Intensity 1995 - 2009 by Sectors in 18 EU Member
States (LHS) and Energy-efficiency Innovation
Activities of Firms by Sectors in 18 EU Member States (RHS)

Source: WIOD, CIS 2008.

The arguments and brief
discussion in section 3.2 had already suggested — at a macroeconomic level —
that increases in the price of energy were one of the major drivers for energy
saving eco-innovations. An interesting follow-up would be to study whether
firms that use energy rather intensively are more affected by increasing energy
prices and have a higher level of induced energy-saving eco-innovation
activities (bearing in mind that existing capital goods can limit the
opportunity space for the adoption of energy-efficiency technology, etc.).
Unfortunately, the CIS data offers no information on either energy prices or on
how much energy is consumed by firms.

There exist a large
number of studies indicating that, apart from prices, regulation is another
important driver for the adoption of eco-innovation in general. The
price-induced innovation argument can be ‘translated’ to environmental
regulation that induces technical change.[9] Early empirical
evidence that regulation triggers eco-innovations is given by Lanjouw and Mody
(1996). They associate international patenting behaviour regarding
environmentally related technologies with pollution-abatement spending in
different countries. Jaffe and Palmer (1997) take the R&D process into account
as well as the outcomes of inventive processes (measured with patent
applications) and do not find a statistically significant effect of
pollution-control expenditures on patenting activities. In contrast to this
study, Brunnermeier and Cohen (2003) find a link between pollution-abatement
spending and successful patent applications related to environmental
technologies. Popp et al. (2010) contains a detailed and comprehensive survey
of this literature.

In
contrast to the literature on the drivers of eco-innovation adoption, a much
less clear-cut prediction is provided regarding eco-innovation’s impact on
competitiveness. The large body of research on the competitiveness impact of
eco-innovation adoption in general is mostly focused on the role played by regulation
(e.g. the very early literature begins in the 1980s after the United States and other highly industrialised countries had started to regulate local water and air
pollutants; for instance, sulphur dioxide (SO2)).

Christiansen
and Haveman (1981) associate an 8–12 % slowdown in U.S. productivity between 1965 and 1979 with environmental regulations. Other studies, like Gollop
and Roberts (1983) or Greenstone (2002), also find that regulation has negative
effects on economic performance. Jaffe et al. (1995), in a comprehensive
survey, con­clude that overall there was relatively little evidence to support
the hypothesis that environmental regulations have had a large adverse effect
on competitive­ness. Several sectoral studies on how firms’ productivity is
affected by environmental regulation appear to reach similar mixed and
inconclusive results: Berman and Bui (2001) find that for U.S. oil refineries,
regulation is associated with a ‘substantial’ investment in pollution-abatement
capital and productivity growth in the more stringently regulated regions;
conversely, Gray and Shadbegian (2003) find the opposite is the case for pulp
and paper plants, again in the U.S.; however, Boyd and McClelland (1999), based
on a new (regression-free) methodology, find some evidence for
productivity-decreasing effects of abatement technology in the paper industry;
Aiken et al. (2009) does not find negative effects of pollution abatement on
the productivity of several sectors in the U.S., Germany, Japan, and the
Netherlands. In a more recent contribution, Rexhäuser and Rammer (2011) use
German CIS data — distinguishing between regulation and non-regulation-induced
eco-innovations (these further broken down into pollution-preventing ones and
those that reduce energy and material use) — finding productivity-enhancing
effects at firm level but only for energy and material-saving technology
adoption.

              3.3.2. Adoption of energy-saving technologies

The choice to introduce
energy-efficiency technology is expected to be driven by environmental
regulation and increasing prices for energy in the first place. For regulation,
the CIS data offers firms’ responses to the question whether energy-saving
process technology was introduced to meet regulatory requirements or whether it
was introduced because regulation was expected to come into force in the
future. For energy prices, however, the CIS data unfortunately offers no
information.

Examples of other
potential determinants of eco-innovations reported in the CIS data are whether
the innovation was introduced in response to demand by customers, due to
voluntary environmental agreements by the firm or due to public subsidies for
environmental technology. There are also such indicator variables as whether
the firm has introduced any other process innovation or new products, exports
to European countries or to world markets (which can be seen as a proxy for
exposure to international competition).

Given the discrete
nature of a firm’s decision whether or not to introduce environmental process technology,
a discrete choice (probit) model estimates the probability of introducing
energy-saving process technology, controlling for firm-specific characteristics
(such as firm size and sector affiliation) and, of course, the determinants for
having introduced eco-innovations the firms reported (see Annex 2).

In line with previous
research, the analysis supports the view that environmental regulation is a key
driver of eco-innovations (the adoption of energy-saving process innovations in
this case). For more than 46 000 firms across 16 European countries[10], the model
estimates that those firms that reported they had introduced eco-innovations
due to environmental regulation have (on average) an 11.70 percentage points
higher probability of adopting energy-efficiency technology than those firms
that did not introduce such innovations due to regulation (see Annex 2). The
mere expectation of further regulation increases by 9.56 percentage points the
probability of adopting energy-saving technology. However, the results differ
across countries. The effect of regulation is found to be greater in Romania (25.9 percentage points), Slovakia (24.8 percentage points), and Bulgaria (24 percentage
points). In contrast, the effect is very low but still significant in Italy (4.7 percentage points).

Other important
determinants are voluntary environmental agreements by firms and the adoption
of other process innovation. Firms that reported voluntary environmental
agreements as the reason for eco-innovation adoption have (on average) a 17.0
percentage points higher probability of adopting energy-saving innovation
compared to firms where this was not the case. The effect of having introduced
another process innovation boosts by 13.2 percentage points the probability of
adopting an energy-saving innovation; a possible interpretation for this is
that energy-saving process technology is to some degree adopted together with
conventional process technology. The effect that introducing new products has
on the probability of adopting energy-saving innovation is also positive but
smaller (+5.3 percentage points).

Firms exporting to other
European countries or to world markets have higher probabilities of adopting
energy-saving innovations but in no case is this statistically significant.
Interestingly, the two export dummy variables were statistically significant in
a different model specification, not controlling for the introduction of new
products and other process innovations. This result suggests there might be an
indirect link between the internationalisation of EU firms and the adoption of
energy-efficiency innovation — meaning that (exporting) internationalised firms
tend to  be more innovative (introducing new products or adopting conventional
process technology), this being  associated with the adoption of energy-saving
innovations. Anticipating the results in the next section, an example would be
a firm that introduces a new product embodying energy-saving features.

              3.3.3 Market success of energy-efficiency product innovators

The existing literature
largely focuses on the adoption of energy-efficiency-improving technologies
(especially if regulation-induced) and the impacts on measured productivity at
firm, sector or aggregate level. Unfortunately, the CIS data does not make it
easy to study the impact of eco-innovation on productivity measures such as
total factor productivity. With CIS it is possible only to study the impact on
rather rough productivity measures, such as turnover or turnover per worker.
Moreover, the non-availability of important factors such as capital use or
energy further complicates matters. The non-availability of capital data is
problematic since capital is expected to be correlated with the adoption of
energy-efficiency technology. Firms that have a higher capital endowment also
need more energy inputs to operate capital goods and therefore (if energy
prices are high) may find a need to replace capital goods by more
energy-efficient ones. In summary, in a standard regression the effect of
energy-efficiency-technology adoption could therefore be biased.

Rennings and Rexhäuser
(2012) made several attempts to circumvent these problems (e.g. by proxying
capital by lagged firm turnover). The regressions performed seem to suggest
that energy-saving process innovation adoption has only minor, if any, effects
on the growth rates of turnover or turnover per worker.

This section takes
another approach to studying the impact of energy-efficiency innovation
activities on the performance and competitiveness of EU firms. A major — and
largely neglected — aspect of competitiveness and eco-innovations is whether
‘green’ product innovations lead to a better competitive position of the
innovators. In what follows, the competitiveness of product innovators will be
studied using firms’ innovation success which is measured, as is commonly done,
by the share of new products in firms’ total sales.

Innovation success is
measured as the sum of the turnover share of market novelties in total sales
plus the share of new products introduced into the market that are new only to
the firm (reported in percentage points in CIS). The CIS data also offers
information on whether the product innovations of firms allow their customers
to save energy. For instance, the data shows (as expected) that manufacturing
firms lead in the introduction of product innovations that allow their
customers to save energy but that other firms also have important energy-saving
innovation activities. Around 15 000 firms (more than 9 250 in manufacturing) across
17 EU countries[11] reported
having introduced newly developed products on the market between 2006 and 2008.
New products account for around 28 % of the firm’s total sales on average
(both for the whole 15 000 and for manufacturing firms only). However, 41 %
of the manufacturing firms reported energy-saving product innovations, against
38 % in the whole sample of product innovators (see Annex 3).

The central question
addressed here is then the extent to which the introduction of energy-efficient
products by firms is valued by the market and whether this translates into greater
firm success compared to conventional product innovators.

One of the major
determinants of innovation success is to what extent a firm is engaged in
innovative activities. A firm that invests more in R&D will in principle have
a higher share of new products in total sales. Moreover, firms that are
continuously engaged in R&D activities may also be more innovative as well
as those that cooperate with other firms, customers or research institutes.
Firms owned by domestic groups or belonging to foreign multinationals may also
have access to external knowledge. The economic literature also offers evidence
of the effect of other variables. For instance, innovative outputs tends to
increase with firm size, but that this relationship follows a less than
proportionate rate (see for instance Scherer (1965) or Acs and Audretsch
(1988)). These are the main variables serving as controls in the regression
analysis (see Annex 3).

In surveys, firms often
report rather ‘round’ numbers if they are asked to state a percentage number,
for instance because they simply do not know the exact number. This was also observed
in the CIS data on innovation success. The dependent variable in the regression
was therefore transformed into a categorical variable recording innovation
success in 10 equally distributed intervals. A sensitivity check has shown that
this rearrangement has only a very small impact on the results. The analysis
reported here is restricted to European firms in the CIS that stated they had
introduced newly developed products on the market (as a large number of
non-innovator firms report missing values for several control variables).

The regression analysis
provides  evidence that innovators that introduce new products into the market,
allowing their customers to save energy, are more successful innovators.
Compared to firms which introduce only conventional product innovations into
the market, eco-product innovators have on average a 2 percentage points higher
share of product innovations in total turnover. At aggregate level, the mean
share of turnover that is earned by selling new products would rise from
approximately 28 to 30 per cent. This may seem to be a small percentage at
first glance but individually the effect can be higher (see Figure 3.24) and
mostly importantly may represent a significant competitive advantage.
Eco-product innovators in manufacturing sectors enjoy a 2.6 percentage point
increase in innovation success compared to conventional product innovators. For
manufacturing firms, this effect is illustrated graphically below.

Figure 3.24 –
Innovation Success in Manufacturing Sectors

Source: CIS 2008.

The
figure predicts the likelihood of a certain level of innovation success being
recorded and compares firms that introduced energy saving product innovations
with those that did not, controlling for any other differences in innovation
success. The interpretation of these density plots is as follows: For ‘green’
product innovators, the likelihood of levels of innovation success from zero up
to, say, 25 per cent being recorded is smaller compared to conventional
innovators. Conversely, the likelihood of eco-product innovators being recorded
at levels above 25 per cent, but most importantly between 25 and 40 per cent,
is higher for ‘green’ innovators compared to non-green innovators.

Overall, there seems to be evidence that product
innovators introducing energy-saving products on the market enjoy higher sales
generated by product innovation compared to conventional product innovators.
This, of course, may also reflect an important competitive advantage.

3.3.4.
The internationalisation and competitive position of EU firms in  ‘green FDI’

Energy efficiency and related environmental
goals are global challenges presenting many business opportunities for EU
firms. This subsection uses the fDi markets database to analyse the
internationalisation and competitive position of EU firms and some EU leading
industries in the area of environmental goods and services. The analysis
focuses on cross-border greenfield investments in an environmental-technologies
cluster related to the provision of environmental goods and services (Golub et
al. 2011). The assignment of greenfield FDI to the environmental cluster is
done at the project level. For example, particular FDI projects within the
machinery industry are included if they relate to environmental goods (e.g. if
the project consists of new production facility for water-treatment systems). Another
example is the electronics industry where projects related to solar modules
from part of the environmental technology cluster. This classification entails
a very large overlap with Eurostat’s definition of Environmental Goods and
Services Industries. In particular, it includes both the main
environmental-protection industries, i.e. waste and wastewater treatment, and
the resource-management industries, i.e. alternative-energy generation
(Eurostat, 2009). In addition, the definition also includes several investments
related to what Eurostat calls ‘connected’ products such as wind turbines.

Table 3.8 presents the amounts (in million USD)
of green FDI projects undertaken by EU MNEs across four main sectors of
environmental technology in the period 2007-2011 and compares them with the
activities of major competitors (MNEs from the US, China and Japan). Renewable
energy is clearly the dominant industry in terms of the amount of green FDI
(374 000 million USD worldwide over the period 2007-2011, accounting for
4/5 of all green FDI projects). In terms of the common industry classification,
the renewable-energy industry would be part of the electricity, gas and water
supply sector – NACE E according to NACE Rev1.). Other important industries for
green investment projects are also found within manufacturing, namely the
electronic-components industry (48 000 million USD worldwide, a share of
10 % of the total green FDI), the engines and turbines industry (with a 4 %
share of the total worldwide green FDI). Industrial machinery accounts for a
smaller share (around 1 %) of the worldwide green FDI but includes a
considerable number of cross-border FDI projects (around 250 projects worldwide
in the period 2007-2011 — not reported in Table 3.8, comparable to the number of
green FDI projects in the engine and turbine industry over the same period).

The prominence of these industries stems from
the fact that companies in these sectors build the equipment needed for
alternative forms of power generation (FDI projects include plants producing
wind engines and turbines or the electronic components of solar panels). The
remaining green FDI is attributed to several sectors (e.g. Metals, Chemicals,
Business Service), each with much lower individual shares.

Table 3.8 - Position of
EU companies in green cross-border investment projects relative to the US, Japan and China (2007-2011, million USD)

|| || EU total || intra-EU || extra-EU || US || Japan || China || RoW || WORLD

Alternative/Renewable || inv. || 236820 || 116053 || 120767 || 47873 || 20145 || 11001 || 58211 || 374049

Energy || share || (63.3) || 31.0 || (32.3) || (12.8) || (5.4) || (2.9) || (15.6) || 79%

Electronic || inv. || 22811 || 6191 || 16620 || 9824 || 2896 || 2449 || 9962 || 47943

Components || share || (47.6) || (12.9) || (34.7) || (20.5) || (6.) || (5.1) || (20.8) || 10%

Engines & Turbines || inv. || 12719 || 1931 || 10788 || 1109 || 932 || 3580 || 1868 || 20208

|| share || (62.9) || (9.6) || (53.4) || (5.5) || (4.6) || (17.7) || (9.2) || 4%

Industrial Machinery, || inv. || 2448 || 392 || 2056 || 911 || 1101 || 28 || 420 || 4908

Equipment & Tools || share || (49.9) || 8.0 || (41.9) || (18.6) || (22.4) || (.6) || (8.6) || 1%

Others || inv. || 14251 || 5229 || 9022 || 2720 || 2796 || 653 || 5942 || 26362

|| share || (54.1) || (19.8) || (34.2) || (10.3) || (10.6) || (2.5) || (22.5) || 6%

Overall Total || inv. || 289048 || 129796 || 159252 || 62438 || 27870 || 17711 || 76402 || 473469

|| share || (61.0) || (27.4) || (33.6) || (13.2) || (5.9) || (3.7) || (16.1) ||

Note: EU is
EU-27. Industry classification of fDi markets database.

Source: fDi markets
database.

Overall, leading EU manufacturing and services
firms in green industries are highly internationalised and seem to be well
positioned in global competition. For the environmental-technologies cluster as
a whole, EU companies accounted for almost 2/3 of green FDI by MNEs worldwide
in the period 2007-2011 (when Intra-EU FDI is also included). Around 55 %
of the EU’s green FDI correspond to extra-EU investments, 160 000 million
USD in the period 2007-2011. This is almost 3 times the amount of outward green
FDI by US MNEs over the same period.

Among the green industries shown in Table 3.8,
EU companies are best positioned in Alternative/Renewable Energy and in the
engines and turbines industry (with a share of close to 2/3 of the green FDI
worldwide in both sectors). EU companies lead international investment
activities in these industries and wind-turbine manufacturing firms in
countries such as Denmark, Germany and Spain play a leading role. The emergence
of Chinese wind-turbine manufacturers (with about 18 % of FDI worldwide)
is reflected by the fact that four of the ten leading companies (in terms of
installed capacity) are from China and some of them have already
internationalised their operations via cross-border projects.

In the other two main sectors for green FDI, EU
companies have a somewhat lower share, but EU MNEs are still global
frontrunners. For instance, within the broader electronics industry EU
companies managed to occupy a niche and develop a competitive edge in photovoltaic
components, at least when judged by their international investment activity. At
the same time, it should be stressed that according to sales figures European
(as well as US) companies are facing intense competition from Chinese
solar-panel producers. China enacted its renewable energies law in 2006, aimed
at reducing energy dependence and CO2 emissions but also at
developing domestic production capacities and internationally active firms.

EU outward green FDI is preponderant in all
sectors except for Alternative/Renewable Energy, in which Extra-EU and Intra-EU
investments are roughly equal, showing the importance of the European single
market for this sector. Outside the EU, the main host country for cross-border
investments by EU firms in environmental technologies is the United States
which accounts for a quarter of total projects (the prominent role of the US as
destination is also found in general for FDI by EU multinationals, see Chapter
4 of this report). In second and third position come two other large markets,
namely India (6.3 % of projects) and China (4.6 % of projects).

Table 3.9 presents worldwide green FDI in the
period 2003-2011 per major host economy (in percentage). The EU attracted more
than a third of all green investments globally over the period 2003-2011. This
makes the EU the major host economy for green cross-border investments, ahead
of the US (12 %), China and India. However, the EU as a whole appears to
have lost some of its attractiveness for green FDI in the last 4 years (the
share of green FDI located in the EU declined to below 40 %, compared to
the exceptionally high pre-crisis level of 55 % in 2007). Similar trends
are observed in overall FDI, the subject of a thorough analysis in Chapter 4.

Table 3.9 - Major host economies for green
cross-border investments, 2003-2011, shares of global green FDI
(in percentage)

Destination Country || 2003 || 2004 || 2005 || 2006 || 2007 || 2008 || 2009 || 2010 || 2011 || average 2003-2011

EU-27 || 21.7 || 34.4 || 36.8 || 44.1 || 54.5 || 44.1 || 37.0 || 37.9 || 39.0 || 40.8

UK || 2.3 || 11.5 || 4.7 || 4.3 || 7.1 || 5.8 || 8.7 || 7.2 || 8.6 || 7.0

Germany || 1.6 || 2.5 || 5.2 || 3.5 || 3.9 || 5.3 || 4.8 || 6.3 || 6.9 || 5.1

Spain || 0.8 || 4.1 || 6.1 || 4.6 || 7.3 || 5.7 || 3.9 || 4.3 || 2.5 || 4.5

France || 3.1 || 0.0 || 4.2 || 7.3 || 7.1 || 9.0 || 3.0 || 1.4 || 2.2 || 4.5

Italy || 1.6 || 0.0 || 1.9 || 0.8 || 4.1 || 3.3 || 4.6 || 4.3 || 3.0 || 3.3

United States || 4.7 || 4.1 || 2.4 || 5.7 || 8.8 || 12.4 || 16.3 || 16.8 || 15.1 || 12.2

China || 6.2 || 11.5 || 4.2 || 5.7 || 8.2 || 8.2 || 7.6 || 8.5 || 5.3 || 7.3

India || 3.1 || 3.3 || 2.8 || 7.6 || 2.1 || 4.5 || 4.0 || 4.0 || 6.1 || 4.5

Canada || 1.6 || 3.3 || 3.8 || 2.2 || 0.4 || 1.7 || 2.4 || 5.7 || 4.8 || 3.1

Brazil || 15.5 || 0.0 || 1.9 || 2.7 || 1.7 || 1.6 || 0.7 || 3.3 || 4.0 || 2.7

Other Countries || 47.3 || 43.4 || 48.1 || 32.2 || 24.2 || 27.5 || 31.9 || 23.9 || 25.7 || 29.5

Overall Total || 100.0 || 100.0 || 100.0 || 100.0 || 100.0 || 100.0 || 100.0 || 100.0 || 100.0 || 100.0

Source: fDi markets
database.

Figure
3.25 shows the trends in cross-border investments in green technologies in the
EU market (including both intra-EU and extra-EU projects), over time covering
the period from 2003 to 2011. In this period, about two thirds of the green FDI
correspond to intra-EU investments (a pattern found for EU inward FDI in
general, see Chapter 4 of this report). This pattern is also observed across
the main four industries for green FDI projects (presented in the right-hand
panel of the figure), except for the electronic components industry, for which
the extra-EU investments are predominant.

Figure 3.25 - Green
cross-border investment undertaken in the EU-27 (left panel) and green cross-border
investment in the EU market in leading green technologies industries (right
panel), 2003-2011

||

Source: fDi markets
database.

The significant decline in green FDI in the EU
in 2009 and 2010 (Figure 3.25, left panel) was mainly due to a sharp drop in
investment and projects in the renewable-energies industry (Figure 3.25, right
panel, right axis). The renewable-energies industry was also driving the
recovery observed in green FDI in the EU in 2011. The number of jobs created by
new cross-border projects in environmental-technology industries closely
follows the trend in investments, though the number of jobs created remained
below the 2007 level in 2011.

Despite the recent overall reduction in
environmental-technology investment activities in the EU market, there is
overall a clear increase in the importance of green technologies in the main
industries analysed. Figure 3.26 (left panel) shows that renewable energy FDI
has been outperforming cross-border FDI in projects related to oil, coal and
natural gas in the EU. The share of renewable energy projects in total energy
projects (renewable and conventional) surpassed 70 % in 2011.

Figure 3.26 - Greening
of cross-border investment in the EU-27, selected industries, 2003-2011

||||||

|| || ||

|| || ||

|| || ||

|| || ||

|| || ||

|| || ||

Source: fDi markets database.

Within the other major green-technology
industries, the share of environmental-technology projects in total EU
cross-border investment projects also increased substantially, with the
exception of the industrial-machinery industry. In the engines and turbines
industry, the share of environmental-technology projects more than tripled from
25 % in 2003 to more than 75 % in 2010 (Figure 3.25, right panel).
The trend is similarly positive in the electronics-components industry.

              3.4. Policy Implications

This chapter studied energy content in exports
and energy-efficiency trends over the last 15 years. Their impact on
competitiveness was analysed at country, sector and firm level in the context
of key economic developments such as the globalisation of industrial activities
and investments and improvements in technology and eco-innovation.

The developments in energy efficiency were first
studied at an international level. Overall energy-efficiency improvements were observed
in almost all countries over the period 1995-2009. In Europe, the EU-12
economies improved significantly their initial low levels of energy efficiency
and the European Union as a whole reinforced its lead in terms of overall
energy efficiency. The analysis highlighted the role of the substitution of
energy for capital —in the sense of a more energy-efficient technology embodied
in capital goods — that was observed over time in almost all countries.

Increasing global competition and cross-border
integration of production chains are developments with far-reaching social,
political and economic consequences. The overall increase in the relative price
of energy is one of its many side effects, often seen as partly due to the
increasing energy demand from developing countries. The rise in the price of
energy and volatility levels have significant and highly differentiated impacts
on the competitiveness of countries, sectors, firms or households.

The analysis in section 3.2 showed that for EU
countries (as a whole) globalisation appears to also represent additional
channels for minimising the negative competitiveness effects of the
energy-price increases. Overall, EU countries have been able to export more and
at the same reduce significantly the energy embodied in their exports, in
particular the proportion of energy that is sourced domestically.

The analysis covered EU-12, EU-15, US and Japan and showed that energy use per unit of exports declined in European (particularly in
EU-12) countries over time in the period 1995-2009. This contrasts with the
increase in the energy embodied in one unit of exports observed in Japan, and to a smaller extent in the US, over the same period.

As expected, the share of energy content in
exports sourced from foreign countries (i.e. energy embodied in intermediate
imports) has been rising everywhere. The WIOD database shows that EU countries
have been leading in this — globalisation induced — upward trend and already
have a higher share of foreign-sourced energy embodied in exports compared with
Japan, a country that also has a high external dependency on fossil fuels.
The importance of emerging economies such as Brazil, Russia and in particular China as sources of the energy embodied in the exports of the advanced economies analysed
has been growing over time.

As a result, the domestic-energy content in
total exports decreased in the European economies. For the EU-12, this is due
mainly to a significant drop in the energy incorporated domestically in
manufacturing exports. In the EU-15, the most important contribution came from
the drop in the domestic-energy content in service exports.

Along with globalisation of production and
increasing vertical specialisation, the European economies have overall reduced
in relative terms their vulnerability to potential external-competitiveness
losses as a result of an increase in the relative price of energy. The relative
weight of energy in their inputs into the foreign content of the generality of
their trade partners’ exports decreased overall in the period 1995-2009. The
EU-15 as a whole, together with Japan, have the lowest relative weight of
energy inputs in the total foreign inputs incorporated in exports globally. The
EU-12 as a whole achieved the greatest reduction in the relative weight of
energy inputs in the foreign content of its trade partners in WIOD.

Manufacturing is at the crossroads of globalisation
and energy efficiency. Manufacturing transforms primary energy inputs into
final energy products, uses energy in the transformation of materials into
products, and many of its sectors and firms are at the forefront of the
internationalisation of production chains and lead in eco-innovation activities
and investments.

An index-decomposition analysis has shown that
manufacturing in the European Union moderately increased gross output while at
the same time maintaining energy use fairly constant due to continuous technical
improvement in the period 1995-2009. Structural changes were negligible in this
period for the EU as a whole.

Japan, like the EU a
world leader in energy efficiency in manufacturing, did not improve technical
efficiency in this period (the observed slight reduction in energy use is due
to a shift to less energy-intensive manufacturing sectors, as output has
remained fairly constant over the period analysed). US manufacturing increased
output and improved technical efficiency, but in both cases less than in the
EU.

Manufacturing output
increased and technical efficiency improved in the very large majority of the
EU-27 Member States but there are significant variations in performance. The
highest increases in manufacturing output were observed in the EU-12 countries
and Ireland, and these were also the countries that tended to achieve the
greatest improvements in technical efficiency. With only a few exceptions,
there was a shift towards less energy-intensive sectors in the EU-12 Member
States.

Section 3.4 analysed
data (from the Community Innovation Survey) showing that EU firms that
introduce new products with energy-saving features tend to be more successful
innovators, particularly in the case of manufacturing firms. Controlling for
other determinants of innovation success in the market, these eco-innovators
sell more new products (in terms of the firm’s total sales) than conventional
innovators, which may represent an important competitive advantage.

The analysis has also
shown that, overall, EU firms are leading in the growing phenomenon of
internationalisation and in cross-border ‘eco-investment’ in clean and more
energy-efficient technologies and products and services, exploiting many
business opportunities offered by the global environmental and societal goals
and challenges ahead. For instance, EU firms accounted for almost 2/3 of the
FDI by MNEs worldwide in the important area of renewable energy in the period
2007-2011. They are also global frontrunners in many other eco-technologies
(such as Engines & Turbines) associated with the provision of environmental
goods and services. However, international competition is increasing, including
from MNEs of emerging economies.

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Annex
1: Index Decomposition Analysis

This annex describes the
(Log Mean Divisia index) decomposition method used in Section 3.4 to study
energy-efficiency performance in the various countries over time. The
decomposition of an economic index — e.g. energy intensity or energy use — into
sub-indices helps in understanding the different economic factors behind the
changes in the index. Three sub-indices were considered: i) economic growth,
ii) structural change, and iii) technical change.

Consider the following
variables for a given country and i=1,…N sectors in years t=0,..,T

Variable || Description

||

Yt || Output in volume of the country in year t

Yt,i || Output of sector i in year t

Et || Total energy use of a country in year t (Et= )

Et,i || Energy use of sector i in year t

It =Et / Yt || Energy intensity of the country in year t

It,i =Et,i / Yt,i || Energy intensity of sector i in year t

St,i =Yt,i / Yt || Share of sector i in the country’s output

||

The impact of economic
growth on the index is called the ‘scale effect’. It describes how the index
would have changed if the other two factors had remained fixed (i.e. no structural
and technical change had taken place). The composition and technical effects
are defined in a similar way. In a simple Laspeyres index decomposition (see
e.g. Ang and Zhang, 2000), the scale effect can be obtained by holding fixed
the sectoral energy intensities and weights (St,i and It,i
at the base year, 1995 in this case) in the calculation of the index; the
‘composition effect’ holds Yt and It,i
fixed in order to isolate the impact of the change in St,i ;
and the ‘technical effect’ holds Yt and St,i
fixed:

The problem with this
simple index decomposition is that it leaves a residual that is difficult to
interpret. This problem does not appear in the Log Mean Divisia index
(developed by Sato, 1976). This decomposition is similar to the Laspeyres method
except for the use of a (logarithmic mean) weighting function on the energy
used. Let =
Et,i / Et be the share of a country’s total
energy that is used by sector i. The logarithmic mean of  is
calculated as:

Note that when  the
logarithmic mean is equal to  (including
when =0).

The Log Mean Divisia
index decomposition for energy use is computed as follows (see Ang and Liu,
2001 for a detailed discussion of the properties of this decomposition):

Annex
2: Estimation results for energy-efficiency technology adoption

Table A.1: Description of the variables used

Variable || Description

EN\_INNO=1,0 || 1 if firm introduced energy saving process innovations, zero otherwise

RD\_INT || R&D expenditures in thousands of Euro per employee

PC\_INNO=1,0 || 1 if a firm has introduced a process innovation; zero otherwise

PD\_INNO=1,0 || 1 if a firm has introduced new products; zero otherwise

ln\_SIZE || natural logarithm of the number of employees

REG=1,0 || 1 if firm introduced an environmental innovation in response to existing environmental regulations or taxes on pollution; zero otherwise

REG \_EXP=1,0 || 1 if firm introduced an environmental innovation in response expected further regulation; zero otherwise

SUBS=1,0 || 1 if firm introduced an environmental innovation in response to governmental grants or subsidies; zero otherwise

DEMAND=1,0 || 1 if firm introduced an environmental innovation in response to market demand; zero otherwise

VOLUNT=1,0 || 1 if firm introduced an environmental innovation in response to voluntary environmental agreements; zero otherwise

ENV\_MANAG = 1 || 1 if firm has introduced environmental management practices; zero otherwise

GROUP\_DOM=1,0 || 1 if firm is affiliated in an domestic enterprise group; zero otherwise

GROUP\_FOR=1,0 || 1 if firm is affiliated in an foreign enterprise group; zero otherwise

EXPORT\_NATIONAL || 1 if firm sells into national market; zero otherwise

EXPORT\_ EUROPE || 1 if firm exports into the European market; zero otherwise

EXPORT\_WORLD || 1 if firm exports into the world market; zero otherwise

Source: CIS 2008.

Table A.2 reports the
marginal effects (at means) for the probit model estimation

,

where the vector x
includes all right hind side variable and Φ denotes the (cumulative)
standard normal distribution. The marginal effects at means describe by how
much the probability of observing EN\_INNO = 1 changes if the variable of
interest changes by one unit observed at the mean of this variable. For a
binary dummy variable, a change from zero to one is considered. Sweden and Finland were omitted due to missing data.

Model (1) includes the
standard determinants of eco-innovations while model (2) studies the robustness
of these variables when conventional process-technology adoption is introduced
as well as product innovation.

Table A.2: Estimation Results for Energy-efficiency Technology Adoption

|| || || || ||

Dependent  Variable || (1) || || (2)

EN\_INNO || Marginal Effect || Std. Error || || Marginal Effect || Std. Error

|| || || || ||

|| || || || ||

RD\_INT || -0.0005 || (0.0006) || || -0.0005 || (0.0005)

PC\_INNO || || || || 0.1315\*\*\* || (0.0062)

PD\_INNO || || || || 0.0525\*\*\* || (0.0052)

ln\_SIZE || 0.0313\*\*\* || (0.0020) || || 0.0265\*\*\* || (0.0019)

REG || 0.1290\*\*\* || (0.0077) || || 0.1176\*\*\* || (0.0074)

REG\_EXP || 0.1029\*\*\* || (0.0081) || || 0.0956\*\*\* || (0.0080)

SUBS || 0.0856\*\*\* || (0.0097) || || 0.0804\*\*\* || (0.0096)

DEMAND || 0.1138\*\*\* || (0.0078) || || 0.1006\*\*\* || (0.0076)

VOLUNT || 0.1811\*\*\* || (0.0082) || || 0.1699\*\*\* || (0.0078)

ENV\_MANAG || 0.0253\*\*\* || (0.0030) || || 0.0240\*\*\* || (0.0029)

GROUP\_DOM || 0.0103\* || (0.0056) || || 0.0103\* || (0.0057)

GROUP\_FOR || 0.0108 || (0.0068) || || 0.0138\*\* || (0.0069)

EXPORT\_NATIONAL || -0.0019 || (0.0068) || || -0.0119\* || (0.0068)

EXPORT\_EUROPE || 0.0235\*\*\* || (0.0076) || || 0.0083 || (0.0075)

EXPORT\_WORLD || 0.0356\*\*\* || (0.0074) || || 0.0108 || (0.0073)

|| || || || ||

Observations || 46160 || || || 46160 ||

Observed Probability || 0.2798 || || || 0.2798 ||

Predicted Probability || 0.2282 || || || 0.2231 ||

Pseudo-R² || 0.2237 || || || 0.2422 ||

|| || || || ||

Note: Standard errors appear in parentheses, \*\*\*,\*\*,\* denotes statistical significance at the 1 %, 5 %, and 10 % level, respectively. The models include 20 sector dummies and 15 country dummies.

Source: CIS 2008.

Figure A.1:

Source: CIS 2008.

Annex
3: Estimation results for energy-efficiency technology adoption

Table
A.3: Description of the variables used

Variable || Description

IS || sum of the turnover share of market novelties in total sales and the share of new products introduced into the market that are new only to the firm

IS\_INTERVAL || IS in 10 equal intervals

ESPI=1,0 || 1 if firm introduced product innovations into the market which allow the customers to save energy; zero otherwise

GROUP\_DOM=1,0 || 1 if firm is affiliated to a domestic enterprise group; zero otherwise

GROUP\_FOR=1,0 || 1 if firm is affiliated to a foreign enterprise group; zero otherwise

CONT\_RD = 1,0 || 1 if firm performs R&D continuously; zero otherwise

EXT\_RD=1,0 || 1 if firm acquires R&D services from external partners; zero otherwise

RD\_INT || R&D expenditures in thousands of Euro per employee

COOP=1,0 || 1 if firm is engaged in R&D cooperation with another external partner; zero otherwise

PC\_INNO=1,0 || 1 if a firm has introduced a process innovation; zero otherwise

Source: CIS 2008.

The descriptive statistics for all variables
used in the later regression appear in the following table.

Table A.4: Descriptive Statistics for Innovation Success Analysis

|| || || || ||

|| Variable || Unit || Observations || Mean || Std. Deviation

|| || || || ||

Sample of all Firms || || || ||

|| IS || % of PD\_INNO in turnover || 14877 || 28.582086 || 27.896667

|| IS\_INTERVAL || In 10 equal intervals || 14877 || 3.1453922 || 2.6701385

|| ESPI || 0/1 || 14877 || 0.38099079 || 0.48564664

|| GROUP\_DOM || 0/1 || 14877 || 0.30389191 || 0.459952

|| GROUP\_FOREIGN || 0/1 || 14877 || 0.26698931 || 0.4424016

|| CONT\_RD || 0/1 || 14877 || 0.63783021 || 0.4806437

|| EXT\_RD || 0/1 || 14877 || 0.42300195 || 0.4940523

|| RD\_INT || Euro per employee || 14877 || 6679.6596 || 34722.871

|| EMPLOYEES || Count || 14877 || 484.30295 || 3232.5027

|| COOP || 0/1 || 14877 || 0.53720508 || 0.49863062

|| PC\_INNO || 0/1 || 14877 || 0.58983666 || 0.4918797

|| || || || ||

Sample of Manufacturing Firms || || ||

|| IS || % of PD\_INNO in turnover || 9259 || 27.458473 || 26.344554

|| IS\_INTERVAL || In 10 equal intervals || 9259 || 3.0336969 || 2.5249134

|| ESPI || 0/1 || 9259 || 0.41311157 || 0.49241912

|| GROUP\_DOM || 0/1 || 9259 || 0.2891241 || 0.45338014

|| GROUP\_FOREIGN || 0/1 || 9259 || 0.28610001 || 0.45196112

|| CONT\_RD || 0/1 || 9259 || 0.67566692 || 0.46815041

|| EXT\_RD || 0/1 || 9259 || 0.43762825 || 0.4961213

|| RD\_INT || Euro per employee || 9259 || 5616.1638 || 33443.144

|| EMPLOYEES || Count || 9259 || 429.39356 || 2615.15

|| COOP || 0/1 || 9259 || 0.52727076 || 0.49928271

|| PC\_INNO || 0/1 || 9259 || 0.63818987 || 0.48055021

Source: CIS 2008.

Table A.5 reports the
estimation results of the model:

The vectors s and c
include sector- and country dummies, respectively. Sweden is now included.

Table A.6: Estimation Results: Innovation Success of European Firms

|| || || || || || || ||

Dep. Variable || OLS || || Interval Regression

Innovation || All || || Product Innovators Only

Success || Firms Across all Sectors || || Manuf. Only

|| || || || || || || ||

|| (1) || || (2) || || (3) || (4) || || (5)

|| || || || || || || ||

|| || || || || || || ||

ESPI || 3.0797\*\*\* || || 2.4069\*\*\* || || 2.0818\*\*\* || 2.0333\*\*\* || || 2.5276\*\*\*

|| (0.4283) || || (0.4671) || || (0.4479) || (0.4416) || || (0.5283)

GROUP\_DOM || -0.9404\* || || -1.6176\*\*\* || || -1.5504\*\*\* || -1.7128\*\*\* || || -2.1523\*\*\*

|| (0.5254) || || (0.5734) || || (0.5499) || (0.5470) || || (0.6877)

GROUP\_FOR || -0.4156 || || -0.3844 || || -0.3878 || -0.4777 || || -1.0518

|| (0.5820) || || (0.6332) || || (0.6072) || (0.5961) || || (0.7360)

CONT\_RD || 4.7795\*\*\* || || 3.8565\*\*\* || || 3.5568\*\*\* || 3.2505\*\*\* || || 2.1004\*\*\*

|| (0.4484) || || (0.4943) || || (0.4740) || (0.4739) || || (0.6058)

EXT\_RD || 2.4537\*\*\* || || 2.1714\*\*\* || || 2.1037\*\*\* || 2.1441\*\*\* || || 2.0954\*\*\*

|| (0.4360) || || (0.4757) || || (0.4562) || (0.4504) || || (0.5488)

RD\_INT || 0.0393\*\*\* || || 0.0441\*\*\* || || 0.0446\*\*\* || 0.0500\*\*\* || || 0.0288\*\*\*

|| (0.0059) || || (0.0063) || || (0.0061) || (0.0064) || || (0.0080)

ln\_EMPLOYEES || -2.1662\*\*\* || || -2.3761\*\*\* || || -2.1502\*\*\* || -1.9474\*\*\* || || -1.3668\*\*\*

|| (0.1571) || || (0.1700) || || (0.1630) || (0.1563) || || (0.2112)

COOP || 1.7278\*\*\* || || 0.6406 || || 0.6758 || 0.5427 || || -0.0475

|| (0.4380) || || (0.4782) || || (0.4586) || (0.4560) || || (0.5552)

Constant || 44.2960\*\*\* || || 45.4269\*\*\* || || 42.5886\*\*\* || 41.3524\*\*\* || || 36.4683\*\*\*

|| (1.9737) || || (2.0335) || || (1.9499) || (1.9485) || || (2.2417)

|| || || || || || || ||

ln\_Sigma || || || || || || || ||

ln\_SIZE || || || || || || -0.0506\*\*\* || || -0.0410\*\*\*

|| || || || || || (0.0037) || || (0.0050)

PC\_INNO || || || || || || 0.0395\*\*\* || || 0.0544\*\*\*

|| || || || || || (0.0122) || || (0.0158)

Constant || || || || || 3.2288\*\*\* || 3.4302\*\*\* || || 3.3343\*\*\*

|| || || || || (0.0059) || (0.0185) || || (0.0257)

|| || || || || || || ||

R² || 0.1104 || || 0.0975 || || || || ||

Log Likelihood || || || || || -34984.514 || -34893.501 || || -21284.462

Observations || 17209 || || 14877 || || 14877 || 14877 || || 9259

|| || || || || || || ||

Notes: Standard errors appear in parentheses, \*\*\*,\*\*,\* denotes statistical significance at the 1 %,   5 %, and 10 % level, respectively. The models include 20 sector dummies and 16 country dummies.

|| || || || || || || ||

Source: CIS 2008.

Model specification (1)
uses the innovation success variable (IS) as reported in the questionnaire.
Model (2) is similar to model (1) but considers only product innovators
(estimated by OLS). Model (3) uses the rearranged dependent variable (coded in
ten intervals, OLS). Model (4) corrects for heteroscedasticity (factors that
are expected to have some impact on the (logged) variance (ln\_Sigma) are
reported). Finally, model specification (5) further restricts the sample to
product innovators in manufacturing sectors.

[1]         See
e.g. Berndt and Wood (1975, 1979), Griffin and Gregory (1976), Pindyck (1979),
Rosenberg (1994), Atkeson and Kehoe (1999) or Gillingham et al. (2009).

[2]         The Penn World Table data
offer additional information on gross domestic product (GDP, in 2005 US dollars
and purchasing power parity (PPP) adjusted) as well as the share of GDP that is
saved. The capital stock is constructed using the perpetual inventory method
(see Caselli 2005). A country’s capital stock in period t is K(t) = (1 –
δ)·K(t-1) + I(t), where I(t) is investment (savings) and δ is the
depreciation rate that is assumed to equal 10 percent for each country and
year. The starting value of the capital stock is constructed as K(0) = I(0)·(1
+ g)/(g + δ), where g is the average growth rate of investment in the
first 5 years. A cross check with the Extended Penn World Tables, where capital
data is reported, although only until 2003, indicates a correlation between the
calculated and the real capital stock of 99.71 per cent.

[3]         Brunnermeier and Cohen (2003)
find that international competition is an important determinant of
environmental innovations, see also Section 5 and ECR2010, Chapter 3.

[4]         The energy
embodied in exports that is sourced domestically is given by

where  is the
vector of domestic energy use per unit of gross output (i.e. all elements in
the NCx1 vector e are replaced by zero, except for the country r, - N=35
sector-, elements, see Box 2.1 in Chapter 2 of this report).

[5]        The difference between total and domestic energy embodied in
exports corresponds to energy sourced from other countries (e.g. energy
embodied in intermediate imports) and therefore the share of foreign energy
embodied in exports is calculated as

[6]         Energy
inputs per unit of total WIOD exports can be recorded as the sum of energy
inputs per unit of exports of each economy weighted by the respective shares in
total WIOD exports. A simple analysis consists in decomposing the changes in
the weighted sum to obtain the changes in each of the elements of the weighted
sum (as a result of the changes in the two variables for each country: energy
inputs per unit of exports and shares in total WIOD exports). A more elaborate
analysis would for instance be to use an index or structural decomposition
analysis (see, for example, subsection 3.3.6; this approach is not followed
here).

[7]           The level of
production is measured by the gross output of the various manufacturing
sectors.

[8]           The CIS 2008
reports information about eco-innovation for 22 Members States. However,
microdata is not available for four of them (Belgium, Luxembourg, Austria and Poland). CIS reports the firms’ responses to the question “During the three
years 2006 to 2008, did your enterprise introduce a product (good or service),
process, organisational or marketing innovation with any of the following
environmental benefits: […]’.

[9]         It
can be argued that what environmental regulation does is to drive a wedge
between the market price of polluting inputs and their shadow price (so that
they become ‘loosely speaking’ relatively more expensive). In this sense,
environmental regulation would have the same consequences as a price increase
for the polluting input factors (such as fossil energy sources), making the
concept of induced technical change applicable to green innovations.

[10]          Sweden and Finland were omitted due to missing data.

[11]          Sweden is not included due to missing data.

Table
of content

4. FDI Flows and EU industrial competitiveness. 153

4.1 Trends and structure of EU-27 inward
FDI 155

4.1.1.          Inward FDI trends: Sharp crisis related
contraction and greater role of extra-EU inflows  155

4.1.2.          FDI inflows from non-EU countries:
continued dominance of US investors, but new sources emerging. 157

4.1.3.          Industry structure of EU inward FDI from
non-EU countries: high foreign presence in manufacturing industries. 159

4.2 Determinants of FDI -
locational attractiveness and firm specific factors  160

4.2.1.          Locational attractiveness. 160

4.2.2.          Firm-level determinants of FDI 168

4.3 Host country effects of inward
FDI in the EU-27. 169

4.3.1.          Direct effects of inward FDI 170

4.3.2.          Indirect effects of FDI on productivity
and performance. 175

4.4 Trends and structures of EU-27
outward FDI 184

4.4.1.          EU outward FDI by destinations: a shift
towards emerging markets. 185

4.4.2.          Industry structure of the EU outward FDI:
the EU possesses comparative advantages for FDI in manufacturing industries. 186

4.4.3.          The importance of EU MNEs in the EU-15
countries. 187

4.4.4.          Emerging outward FDI from the new EU
Member States (EU-12) 188

4.5 Home country effects of outward
FDI on EU industry.. 191

4.5.1. Employment effects. 193

4.5.2.          Skill structure. 194

4.5.3.          Technology transfer. 194

4.5.4.          Productivity. 195

4.5.5.          Profitability. 195

4.6 Conclusions and policy
implications. 196

Appendix   201

5. Clusters and Networks. 201

5.1. Introduction.. 201

5.2 Concepts of Clusters, Cluster
organizations and Networks. 201

5.3. Presence and Policy of
Networks. 201

5.3.1. Types of Firm Networks. 201

5.3.2. Public Policy Support to Networks. 201

5.3.2.1. Geographic focus. 201

5.3.2.2. Industry focus. 201

5.3.3. Public Tools. 201

5.4. The Role of Public Policy.. 201

5.4.1. Justification of network programmes. 201

5.4.2. Objectives of network programmes. 201

5.4.3. Operational design of network programmes. 201

5.5. Policy implications. 201

6. Competitiveness developments along
the external borders of the European Union.. 201

6.1. The Rim   201

6.2. Economic situation and
competitiveness of the Rim countries. 201

6.3. Trade relations between the EU
and the Rim.. 201

6.4. Foreign direct investment
effects. 201

6.5. Southern Rim: fostering
North-South and South-South economic integration.. 201

6.6. Eastern Rim: hesitant
integration.. 201

6.7. Labour markets and migration.. 201

6.7.1 The Eastern Rim.. 201

6.7.2 The Southern Rim.. 201

6.7.3 Western Balkans. 201

6.7.4 Norway, Switzerland and Liechtenstein. 201

6.8. Remittances. 201

6.8.1 The Eastern Rim.. 201

6.8.2 The Southern Rim.. 201

6.8.3 Western Balkans. 201

6.9. Labour migration and EU
competitiveness. 201

6.10. Policy implications. 201

7.    Statistical annex.. 201

7.1. Sectoral competitiveness
indicators. 201

4. FDI Flows and EU industrial
competitiveness

The
European Union is a major player in global foreign direct investment (FDI), in
terms of both inward and outward FDI. This reflects not only the potential of
the single market, but also the ability of EU companies in different industries
to successfully compete in markets outside the EU. The crisis has, as expected,
caused a disruption in FDI: the EUʼs share of world (inward) FDI flows
have declined substantially, from 45% in 2001 to 23% in 2010. Outward
investment flows have also dropped significantly and have been accompanied by a
shift of FDI outflows to non-EU emerging markets, less affected by the European
crisis.

The
recent fall in inward FDI flows raises the following questions: what are the
main factors influencing the decision to invest in an EU country, and how can
we boost Europeʼs attractiveness to investors? Despite the conjectural decrease
in inward FDI, the EU is generally considered an attractive location for
foreign investment, with low FDI regulation, a highly educated workforce, and
high productivity levels, to mention but a few of the factors that may make EU
countries attractive to foreign investors. The attractiveness of the EU is well
reflected in the high inward FDI stock in several industries. An empirical
analysis will provide some evidence on the most important determinants.

FDI is
generally expected to have positive direct and indirect effects on the
recipient economy. On the one hand, foreign enterprises directly increase the
capital stock and create employment; on the other, they may bring new
technologies, skills and human capital that can spill over to domestic firms
and workers. The empirical literature for EU countries finds strong support for
positive direct impacts, while the evidence on spillover effects is less
clear-cut. A better understanding of the indirect impact of inward FDI is
important because it opens the door to public interventions. Hence, governments
often provide substantial financial support to attract FDI. The impacts that
FDI has on host economies and firms depend on a wide range of factors, e.g. the
type of investment, the absorptive capacity of the host country, and the size
and other characteristics of firms. It is therefore crucial to gain a clearer
picture of how the benefits of FDI for local firms can be maximised and any
potential adverse effects minimised.

Likewise,
outward FDI is seen as an important engine of economic growth. Multinational
enterprises are larger, and more productive, pay higher wages and have better knowledge,
technologies and managerial skills. They might also gain competitive advantages
by expanding into new markets, through the learning effects of
internationalisation, by reducing production costs and by gaining access to
natural resources, advanced technologies or know-how. While the positive
effects of outward FDI are generally assumed to predominate, there are concerns
about its possible drawbacks, particularly the adverse effects on the domestic
labour market. The theoretical predictions on home market effects are far from
clear-cut and depend on the type of and motive for outward foreign direct
investments and the very specific relationships between the parent company and
its foreign affiliates. The analysis of the effects of inward FDI is completed
by a discussion on the home country impacts of outward FDI.

In
order to better understand the determinants and impacts of inward and outward
FDI in Europe this chapter[1]
provides the following analysis:

·
an overall picture of the main trends and
patterns of EU inward and outward FDI flows at the aggregate, sector and firm level;

·
the factors that influence FDI flows, both
locational factors driving FDI inflows to the EU Member States and the firm
specific factors that in turn account for the internationalisation of firms;

·
the direct and indirect effects of inward EU FDI
on domestic firms and the host country in general;

·
the main findings of the literature on the
effects of outward FDI on the home country of multinational enterprises (MNEs);

Finally, a policy section
discusses a number of debated issues based on the analysis carried out in this
study.

Box 4.1 – Definitions · Foreign Direct Investment (FDI) Foreign direct investment (FDI) is defined as an investment involving a long‑term relationship and reflecting a lasting interest and control by an entity resident in one economy (foreign direct investor or parent enterprise) in an enterprise resident in another economy  (FDI enterprise or affiliate enterprise or foreign affiliate) (OECD, 1996). FDI has three components: equity capital, reinvested earnings and intra-company loan. · Forms of FDI (1) Greenfield investment: establishment of an entirely new firm in a foreign country, including new operational facilities; (2) Mergers and acquisitions (M&A): a complete or partial purchase of an existing firm in a foreign country. · Motives for FDI Market-seeking FDI involves investing in a host country market in order to be closer to customers and to serve that market directly rather than through exporting (ʻhorizontalʼ FDI). Market-seeking investors will rate the attractiveness of a host country mostly with respect to its market size and growth/demand potential, and whether it provides access to both regional and global markets. For non-tradable services (e.g. hotel and catering industry or retail trade), FDI may be the only way to internationalise as there would be no alternatives for accessing foreign markets. Resource-seeking FDI is driven by the need to gain access to natural resources such as oil, gas, minerals or raw materials. Locations qualify as being more attractive the more they provide access to affordable resources, particularly if the domestic supply of such inputs has come under pressure by becoming more expensive. Scarce supply of and growing needs for natural resources explain the EUʼs growing interest in resource-rich development countries and the proliferation amount of respective strategies (for instance the Central Asia Strategy and the Joint Africa-EU Strategy launched in 2007).[2] Strategic asset-seeking FDI aims to gain access to advanced technologies, skills and other highly developed productive capabilities. The aim of this type of investment is to increase the acquiring firmʼs global portfolio of strategic resources and to block competitors from obtaining access. Either way, strategic asset-seeking investors value locations depending on the quality of the scientific, technological and educational infrastructure they provide and on the availability of a rich pool of highly skilled labour. Efficiency-seeking FDI takes place when companies try to exploit economies of specialisation and scope across the value chain (product specialisation) and along the value chain (process specialisation). The company will slice its production chain by allocating different parts (or tasks) to countries that allow low-cost production (vertical fragmentation), particularly where the cost of labour is taken into account. The scope for efficiency-seeking FDI and vertical fragmentation originates from advances in information and communication technology (ICT), trade liberalisation and cost-effective transportation, which enable firms to take advantage of international factor cost differentials. Another key determinant is the competitiveness of local industrial infrastructure and its ability to provide strong subcontracting and business partners.

              4.1 Trends and structure of EU-27 inward FDI

              4.1.1. Inward FDI trends: Sharp
crisis related contraction and greater role of extra-EU inflows

The EU
is by far the largest destination for global FDI. This is primarily the result
of the size of the EU market but it also has to do with its openness to FDI and
the deep economic integration among EU Member States. Over the past decade,
however, the share of global FDI destined for the EU, including intra-EU
investments, has declined substantially, from 45% in 2001 to 23% in 2010, in
favour of emerging economies.

FDI
inflows to the EU were hit significantly by the global recession of 2008/2009. FDI
flows to the EU dwindled in 2008 to half of their 2007 peak value and continued
to decline slightly in 2009 and 2010 (Figure 4.1). Intra-EU flows
continued to decline in 2009, while FDI inflows from non-EU countries recovered
somewhat in 2009. In 2010 total FDI flows to the EU amounted to
EUR 230 bn of which about 60% originated from EU Member States. Although
EU FDI inflows seem to have recovered somewhat in 2011, it seems most unlikely
that in the coming years FDI levels will return to that of the 2007 boom year
when investment activities were fuelled by excessively high stock prices and
overly optimistic business sentiments in some sectors. The current situation
may be better described as a return to ʻnormalʼ levels than a state
of depression.

Figure 4.1 – EU-27 FDI inflows, 2001-2010, EUR
bn

Note: EU
is EU-25 for 2001-2003 and EU-27 for 2004-2010. EU flows calculated as the sum
of EU Member States. Intra-EU flows to Luxembourg are adjusted downwards by 90%
in order to exclude activities of Special Purpose Entities (SPEs). Extra-EU
flows exclude offshore centres (Guernsey, Jersey, Isle of Man, Gibraltar,
Bahamas, Bermuda, British Virgin Islands, Cayman Islands, Netherlands
Antilles).

Source: Eurostat, wiiw calculations.

Until
recently a standing feature of EU inward FDI was that intra-EU flows were much
larger than flows from non-EU countries. The downturn in FDI after the boom
years of 2005-2007 affected both extra-EU and intra-EU inflows but the contraction
was stronger in the case of the latter. As a consequence the share of extra-EU
FDI in total EU inward flows, which until 2006 was less than a third, continued
to increase after 2008. In 2010 the share of FDI inflows stemming from non-EU
investors stood at 40%. This is clearly linked to the depth of the recession in
the EU and the relatively good performance of most emerging economies.

The
severe drop in intra-EU FDI flows seems to be linked to a reduced capability of
European firms to invest abroad. This appears to be the driving force behind
falling FDI activities of European banks whose international expansion plans
have been halted by the economic crisis. Outside the financial sector, the low
intra-EU flows in the period 2008-2011 may primarily reflect the trouble EU
firms are undergoing in this period. Indeed, FDI from outside the EU is not
that affected by the contraction. Furthermore, the declining share of intra-EU
FDI may also reﬂect the natural adjustment towards long-run conditions
after the exceptional increase in intra EU-FDI flows caused by EU enlargement
in 2004 and 2007 and strong economic growth during that period.

              4.1.2. FDI inflows from
non-EU countries: continued dominance of US investors, but new sources emerging

Given
the increased volume of extra-EU inflows it is interesting to have a look at
the main investor countries and potential new sources of FDI. A first observation
is that FDI inflows to the EU from the rest of the world are extremely
concentrated.[3]
The US and the EFTA countries, principally Switzerland, are the largest
investors, accounting for more than half of the total inward FDI stock in 2010.
The leading position of US multinationals in EU inward FDI was largely
unaffected by the crisis: in the period 2008-2010 the US accounted for about
45% of total extra-EU inflows. At the same time the share of the EFTA countries
declined significantly over 2001-2010. A declining trend is also observable for
Japan. Investors from these countries are expected to continue to determine the
aggregate trend in inward FDI from non-EU countries. This is in accordance with
their economic weight and their high degree of integration with the EU.

In
contrast to developed regions, the share of developing regions and transition
economies as a whole increased substantially (Figure 4.2). In value terms
Western Asia is the most important new investor region for the EU, with average
annual inflows amounting to EUR 19 bn in the period 2008-2010[4].
Just to compare, the annual average inflows from developed economies were over
EUR 70 bn in the same period. However, the increasing role of the emerging
markets in inward EU FDI is not only a crisis-induced phenomenon but a longer-term
trend as evidenced by the development of emerging marketsʼ shares in
overall extra-EU inward stocks since 2001.

Figure 4.2 – Share of emerging regions and
countries in extra-EU inward stocks, 2001-2010, shares in %

Note: EU is EU-25 for 2001-2003 and EU-27 for 2004-2010. Shares
calculated on the basis of the inward stocks of the EU-27 aggregate.

Source: Eurostat, wiiw calculations.

The
magnitude of FDI inflows (and also stocks because of the shorter ʻFDI
historyʼ) from emerging regions and countries, including China and India[5],
is likely to grow, but is still rather small. Chinaʼs FDI flows to the EU
increased substantially in 2010, to EUR 4.5 bn[6] (of which
EUR 2.4 bn was destined for Luxembourg).[7] As a
comparison,  FDI inflows from the US amounted to more than EUR 30 bn in 2010.
Furthermore, FDI stocks in 2010 stemming from the US represented 40.5% of the
total extra-EU inward FDI, while Chinaʼs stock of FDI to the EU amounted
to only 1.2%.

The
growing number of greenfield investment projects suggests the prominent role of
China and India as a new source of FDI.[8]
Both countries figure among the main new greenfield investors in the EU. China
and India established 137 and 93 projects, respectively, followed by Russia,
with 44 projects in 2010. The chances are high that in the near future Chinese
firms will also become increasingly active in Europe through FDI and no longer
serve the EU market only via exports.[9]
However, despite the more intensive investment activity of emerging
multinationals, the general trend in inward FDI to the EU is expected to be
driven by traditional investors.

              4.1.3. Industry structure
of EU inward FDI from non-EU countries: high foreign presence in manufacturing
industries

Regarding the structure of inward EU FDI stocks
manufacturing industries and services took 47% and 43% shares, respectively, in
2008 - when excluding the financial sector and other business activities.[10]
This is in line with the structure of EU trade, which is dominated by
manufacturing, with services typically accounting for only 20% of trade.

Among
the manufacturing industries the largest shares of investment stemming from
non-EU countries are to be found in the chemical industry (EUR 98 bn
and 14%) and the food industry (EUR 53 bn and 8%). In contrast, the
automotive (and transportation equipment) industries account only for slightly
more than 3% of the EUʼs inward stocks owned by the rest of the world,
which is a comparatively low share given the industry's high degree of
internationalisation and its great importance in EU trade relations. Turning to
the services industries but leaving aside the important financial sector and
the activities of holding companies, trade and repairs (20%), real estate (6%)
and computer services (4%) emerge as the industries with the largest EU inward
stocks owned by non-EU investors.

In an
attempt to gain an idea of the foreign presence in EU markets, inward stocks
can be compared with the value added generated by the respective industry in
the year 2008. For the EU economy as a whole, the ratio of inward FDI to value
added amounts to 10.9. [11]
This means that non-EU MNEs account for approximately 11% of the EUʼs
value added.

Figure 4.3 – Ratio of
EU inward stocks owned by the rest of the world to value added, by industry,
2008

Note: EU
stocks are stocks of the EU-27 aggregate. FDI stocks and value added excluding
financial intermediation (6895).
Source: Eurostat, wiiw-calculations. The horizontal axis intersects
the vertical axis at the EU average of 10.9 so that the bars of industries with
a lower than average ratio are pointing downwards.

The industry-specific ratio of inward
FDI stocks of MNEs from non-EU countries to value added in the EU economy
suggests that the foreign presence is above the average in manufacturing industries.
In the area of R&D, FDI occur primarily in the manufacturing sector and in
particular in high-tech and medium-high-tech manufacturing sectors (European
Commission, 2012).  It is especially true for capital-intensive branches such
as the chemical industry and the petroleum refining industry (Figure
4.3). Probably due to the large
number of M&As the European mining  industry also
faces a competitive pressure. In
contrast, the FDI to value added ratio is below the economy-wide average for
most services industries (the hotel, transport, storage and communication
industries). This is somewhat unexpected given the fact that in several
services industries, such as the hotel industry, FDI is the only way to enter a
foreign market because market access via exports is not possible. At the same
time it also indicates the importance of the domestic EU enterprises in these
sectors.

              4.2 Determinants of FDI -
locational attractiveness and firm specific factors

Global investment flows have increasingly tended to shift
towards high-growth emerging markets. The recession and the eurozone crisis
have adversely affected FDI flows in Europe. Nevertheless, the EU in general has
maintained its fundamentals (e.g. good institutions, openness, highly skilled
workforce), which can be considered as key determinants of inward FDI. In terms
of investment perception, Western Europe ranks as the second most attractive
region and Central-Eastern Europe as the third most attractive destination
worldwide for FDI.[12]
The heterogeneity of Member States in terms of factors determining FDI inflows reveals
differences between EU countries: several countries have remained among the
most popular investment destinations (e.g. Germany or Poland) while others have
not attracted substantial amounts of FDI for many years already (e.g. Italy). The
literature has investigated extensively what makes a country attractive for foreign
real investors. Below a summary and new empirical evidence are provided.

              4.2.1. Locational attractiveness

FDI
activity depends on a wide range of factors and conditions, including
location-specific (host country) determinants and home country characteristics.
The next section tries to address some of these questions. According
to UNCTAD (1998) the host country determinants of FDI can be classified into
three groups: policy framework for FDI, economic determinants and business facilitation
(see Table 4.1). Several of the determinants listed below have received quite a
lot attention in the literature in the last ten years.[13] However, little
is known about whether the sign and magnitude of the FDI determinants differ according
to (i) the country of origin of the investors (e.g. EU versus non-EU investors),
(ii) the target industry (e.g. high- vs low-tech), (iii) the type of FDI
activity (e.g. production, services, research and development), (iv) the mode
of entry (greenfield FDI or cross-border M&As), (v) the type of FDI (vertical
and horizontal) (vi) the geographical destination (capital region or elsewhere).

The
available empirical findings based on EU countries make it difficult to draw
general conclusions about the source of heterogeneity in the determinants of
FDI for EU countries. This section therefore
also provides some results based on an FDI gravity model
estimation using FDI stocks and greenfield FDI flows from 26 OECD/BRIC countries
to the EU-27 in the period 2000-2010. (Table A.1 in the Appendix shows the
results of the gravity equation estimated in the background study, Falk et al,
2012.) The basic gravity model is augmented by the inclusion of corporate taxes
and labour costs of the host and home country, the impact of EU membership in
2004 and 2007 and the introduction of the euro in some EU countries during the
period 2007-2010. A number of policy factors (e.g. FDI regulation, costs of
starting a business and labour market flexibility indicators) and indicators of
factor endowments (e.g. skills, R&D and broadband penetration) are also included.[14]

Table 4.1. – Host country determinants of FDI

I. Policy framework for FDI

Economic, political and social stability

Rules regarding entry and operations

Standards of treatment of foreign affiliates

General legal and administrative system that shape the structure and functioning of markets (e.g. competition & M&A policies, corporate and labour taxation, product & labour market regulations, IPRs)

International agreements on FDI

Privatization policies

Trade policies (tariffs and non-tariff barriers) and the coherence of FDI and trade policies

II. Economic determinants (by FDI motive)

II. 1 Market seeking

Market size and per capita income

Market growth (potential)

Access to regional and global markets

Country-specific consumer preferences

Structure of markets (e.g. market concentration, entry barriers, pricing)

II. 2 Resource seeking

Availability of natural resources (e.g. oil and gas, minerals, raw materials, agricultural land)

Physical infrastructure (ports, roads, power, telecommunication)

II.3 Strategic asset seeking

Skilled labour and quality of educational infrastructure (e.g. schools, colleges, universities)

Quality of technological and R&D infrastructure (e.g. research institutions, universities, ICT)

Innovation clusters

II.4 Efficiency seeking

Cost and productivity of local labour supply

Cost of raw materials and intermediate inputs

Cost of transport and communication to/from and within host economy

Financing cost

Industrial infrastructure (e.g., subcontracting and business services, supplier industries, industry clusters)

III. Business facilitation

Investment promotion

Investment incentives (tax and financial)

Costs related to corruption and bureaucratic inefficiency

Social amenities (e.g. quality of life)

Infrastructure and support services

Cluster and network promotion

Social capital

Source: Adapted from UNCTAD (1998).

              4.2.1.1.          Policy framework for FDI

The
institutional settings, such as the rules regulating entry and operations, and
the legal and administrative system, are very important factors in determining
every type of investment decision. For instance, FDI barriers (such
as legal, legislative and regulatory frameworks, the strength of investor
protection, foreign ownership restrictions and red tape)
are likely to discourage inward FDI since they lead to
higher investment costs. FDI restrictions have declined considerably in the EU
and they are currently among the lowest in the world,[15] providing a
favourable business environment for foreign companies. Similarly,
the administrative burden on enterprises and product-market regulations in the
host country impose additional costs on businesses and create barriers to entry
for FDI (Azémar and Desbordes, 2010).
In the EU-27 countries there is a significant and negative
relationship between the foreign employment share in the manufacturing sector
and the costs of starting a business. A significant and positive correlation
between the ratio of FDI inflows and the strength of investor protection has
been found for the EU countries. Labour market flexibility is also considered
to have positive impacts on FDI inflows. For instance, based on a sample of 19
EU countries Javorcik and Spatareanu (2005) found that a more flexible labour
market in the host country leads to higher FDI inflow (see
also Bénassy-Quéré et al., 2007, based on OECD data; Dewit, Görg and Montagna,
2009).

Most of
the policy and non-policy factors are excluded from the final specification for
the gravity model on the EU-27, because they are not significant at
conventional significance levels (see explanatory variables in Table A. 2. in
the Appendix). In particular, labour market flexibility, indicators of
intellectual property rights protection and investor protection are not significant
when source and host country fixed effects and common time effects are taken
into account. The cost of doing business and the FDI regulatory index have the
expected negative sign but are statistically insignificant. One reason for the
insignificance of these variables is that the annual time variation is very
small.

Trade
policies, trade agreements and regional integration have significant effects on
FDI flows. Regional preferential trade agreements (RTAs) not only stimulate
trade in goods and services due to the removal of trade barriers but may also
have an impact on FDI flows for the participating countries and on third
countries. The empirical literature strongly suggests that
European economic integration (e.g. EU membership, creation of the European
single market in 1992) has been accompanied by a rising level of foreign direct
investment within the EU, and increased FDI flows from third countries (Pain,
1997; Clegg and Green, 1999; Lafourcade and Paluzie, 2011). The introduction of
the euro is also expected to have a positive impact on FDI flows because of
lower transaction costs and elimination of exchange rate uncertainty. The
gravity model estimation (Table A.1 in the Appendix) finds that the
introduction of the euro and EU membership (2004, 2007) leads to higher FDI
activity among the euro area and EU members. The effect is more pronounced in
the case of countries that joined the EU in 2007, with an increase in FDI
inflow of more than 100% between 2007 and 2010. Previous empirical studies also
found large positive effects of the euro on FDI inflows (Coeurdacier, De Santis
and Aviat, 2009; Petroulas, 2007; De Sousa and Lochard, 2011; and Brouwer et
al., 2008).

The
signature and ratification of double taxation agreements (DTAs) have reduced
barriers to FDI. DTAs deal with the allocation of the taxable capital flows,
dividends, interest and royalties generated by multinational firm activity
(Hallward-Driemeier, 2003). DTAs are expected to have a positive impact on FDI
flows. Since most EU countries had double taxation treaties with other EU
and/or OECD countries at the end of 2010, the expected effects of DTAs are not
likely to be significant for the last decade.

              4.2.1.2.          Economic determinants

The
second group of FDI determinants comprises economic factors which can be
further classified according to the motives for FDI. Surveys
among foreign investors typically find that factors such as the size and growth
of the local market, the presence of suppliers and business partners and access
to international/regional markets are the most important determinants for a
locationʼs attractiveness (UNCTAD, 2011). In the case of the EU-15
countries, market size and a stable investment environment play the most
prominent role. For EU-12 countries, growth of the market is the most important
factor, followed by cheap labour, the availability of skilled labour, a stable
investment environment and the size of the market (see Table 4.2).[16] Results of
the gravity model also confirm this: a 1% increase in the level of GDP in the
EU-27 countries in the previous year leads to an increase in the inward FDI stock
in the current year by 1% on average.

Table 4.2 -
Locational attractiveness: the view of business

|| World || EU-15 || EU-12

Size of local market || 21 || 20 || 12

Growth of local market || 20 || 12 || 19

Stable investment environment || 10 || 19 || 12

Access to regional markets || 10 || 11 || 7

Cheap labour || 9 || n.a || 12

Availability of skilled labour || 9 || 11 || 12

Access to natural resources || 6 || 4 || 8

Access to capital market (finance) || 2 || 6 || 2

Incentives, government effectiveness || 5 || 11 || 6

Follow the leader || 4 || 3 || 3

Total || 100 || 100 || 100

Note: The
table provides the main location factors for attracting FDI for the period
2007-2009 in %.

Source: UNTCADʼs World Investment Prospect Survey (2009).

Among
the economic determinants both cost- and non-cost based factors have been
intensively discussed in the literature. Cost-based
factors such as the unit labour costs and effective average corporate tax rate
in the host country are expected to have a negative impact on bilateral FDI
stocks.

Differentials
in labour costs (unit labour costs, labour taxation) between the home and host
countries play an important role, particularly for vertical or efficiency-seeking
FDI. Results of the gravity model show that a 1
percentage point increase in the unit labour costs of the host country leads to
a decrease in the FDI stock by 1%. Unit labour costs increased over the sample
period on average but the change is highly uneven across EU countries. While
the literature based on data for the EU-10 countries shows that unit labour
costs have a negative impact on FDI inflows into the host country, for the
EU-15 countries a number of studies found that labour costs are not a significant
determinant (Wolff, 2007, for EU-25 and EU-15 countries; de Sousa and Lochard,
2011, for EMU countries; Bellak and Leibrecht, 2011, for 10 EU countries and
the US). This is in contrast with what has been found for the EU-15 in the
current analysis: in some EU-15 countries rising unit labour costs are
considered as a major factor in the slow growth of inward FDI. One explanation
of the higher impact of unit labour costs is the difference in the time period:
the sample used for the current analysis ends in 2010. The increase in unit
labour costs particularly accelerated between 2007 and 2010 in most of the
EU-15. The increase in unit labour costs is associated
with a 3% lower growth rate of the bilateral FDI inward stock as compared to
EU-15 countries with stable unit labour costs. Furthermore, the analysis shows
that high productivity growth together with moderate wage growth plays an
important role in attracting FDI flows in the EU-15 countries.

Regarding
indirect labour costs, such as labour taxation, Egger and Radulescu (2011) found
that average effective taxes on individual earnings have a significantly
negative effect on FDI. Other authors (Head and Mayer, 2004) find negative
effects of the social security contributions and/or labour taxation on FDI
inflows in the EU. With respect to other indirect taxes, Buettner and Wamser
(2009) find that indirect taxes do not play a role for foreign location choice.

Previous
empirical studies largely agree that FDI flows are sensitive to changes in
corporate tax rates in the host and also the home countries. In general, higher
home country tax rates lead to higher FDI outflows, whereas a higher host
country tax rate leads to lower FDI inflows (De Mooij and Ederveen, 2003). On
the other hand, some recent studies based on data for the EU-15 countries did
not find that corporate taxes had a significant impact on FDI activity (e.g.
Hansson and Olofsdotter, 2012, for the EU-15 countries; Egger, 2001, for the
EU-15 countries; Bénassy-Quéré, Gobalraja and Trannoy, 2007, for 18 EU
countries; and Wolff, 2007, for the EU-15 and EU-25
countries). Similarly, using FDI data for 28 OECD countries for the period estimates,
Hajkova et al. (2006) found that the effects of taxation on FDI are
quantitatively small and are much less relevant than other factors such as
labour costs, the regulation of FDI and product markets and openness. In
contrast, studies that explicitly focus on the EU-12
countries find that corporate taxes have a negative effect on FDI activity (Bellak
et al., 2007).

The
results of the gravity model on the effects of taxes on FDI stocks are
difficult to compare with previous studies due to the difference between
country coverage and time period, etc. Corporate tax rates decreased in both
the EU-15 and the EU-12 by 8 and 9 percentage points, respectively, over the
sample period. According to the estimations a 1 percentage
point increase in the effective average tax rate reduces the bilateral FDI
stock by 1.6%. Furthermore, the coefficient on statutory corporate taxes in the
home country are not significantly different from zero, indicating that the
outward FDI stock is not higher in high-tax countries than in low-tax
countries. In addition, the factors of FDI are different when the sample is
split into EU-15 and EU-12 host countries. The results show that corporate
taxes matter only in the EU-12 countries and not in the remaining EU-15
countries. Taking exclusively greenfield investments into account, it has been
found that greenfield FDI is much more sensitive to changes in taxes than total
FDI in both the EU-15 and the EU-12 (See Table A.3. in the Appendix). The
insignificance of corporate taxes for total FDI might be related to the
composition of FDI stocks and flows, since in the EU-15 the bulk of FDI
activity is due to M&As whereas in the EU-12 greenfield investments account
for the most of the FDI flows.

Among
the non-cost determinants a skilled labour force in the host country has long
been recognised as being important to FDI inflows. For the
sample of EU-12 host countries tertiary education has a significant impact. Hence,
investing in education and training helps to attract FDI and to increase the
benefits from FDI. For the EU-15 countries, no significant relationship has
been found. The European Commission (2005) also found that a high qualification
of the workforce in the EU-10 is a more important location factor for
multinationals as compared to the EU-15 countries. Furthermore, when focusing
only on R&D internationalisation human capital, as proxied by the share of
tertiary graduates in technology related fields is important only for the group
of EU-12 countries (European Commission, 2012). A possible explanation is that
the EU-15 countries already have a high proportion of workers with tertiary
education, while in the case of the EU-12 a significant increase in the number
of graduates can be observed during the sample period. The insignificance of
the education variables might also be related to the fact that length of education
quantity is a poor measure of the skills of the workforce in the EU-15. Based
on the sample of OECD countries, Nicoletti et al. (2003) found that the average
number of years of education in the host country is significantly positively correlated
with FDI inflows. Studies investigating the location choice of multinational
companies within a European country also found a positive relationship between
the level of formal qualification of workers and FDI. However, it is important
to be aware that in European countries differences in skill quantitative
measures of skill levels (e.g. average years of schooling) are much less
pronounced than differences in education quality (e.g. PISA scores).

Infrastructure
covers a range of aspects such as transport infrastructure, ICT infrastructure
and electricity generation capacity. In particular, the accessibility of
highways, railways, airports and seaports is an important aspect for location
choice, for all types of FDI. Studies based on regional data for individual EU
countries confirm this (see Cieślik, 2005a; Cieślik, 2005b for
Poland; Barrios, Görg and Strobl, 2011 for Ireland). Based on FDI inflows for eight
EU countries in Central and Eastern Europe, Bellak, Leibrecht and Damijan
(2009) found that information and communication infrastructure is more
important than transport infrastructure and electricity generation capacity.
Using a broader sample of inward FDI activity in EU countries and the US,
Bellak and Leibrecht (2011) confirm that ICT endowment is a significant and
important location factor.

Agglomeration
economies are one of the most important factors affecting firm location
decisions of multinational enterprises. FDI tends to cluster in certain
locations that are characterised by a large share of foreign enterprises. One
explanation for this is that foreign subsidiaries tend to co-locate with
foreign suppliers and foreign customers. Another reason is that foreign firms
may interact with each other rather than with domestic firms if the quality or
the productivity of local suppliers is low (Pusterla and Resmini, 2007).
Another reason for clustering of foreign firms is to take advantage of a common
pool of skilled workers and knowledge inputs and ideas. Previous studies based
on the location choice of foreign firms moving into EU countries found strong
agglomeration effects (e.g. Crozet et al., 2004; Disdier and Mayer, 2004;
Pusterla and Resmini, 2007; Basile et al, 2008; Hilber and Voicu, 2010;
Procher, 2011).

              4.2.1.3.          Business facilitation

The
third group of FDI determinants consists of business facilitation measures,
including investment incentives and promotion, measures directed at reducing
costs linked to corruption and administrative inefficiency, and social
amenities (e.g. quality of life).[17]
Proactive measures aimed at facilitating the business that foreign investors
undertake in a host country include investment incentives and investment
promotion. Investment promotion mainly reduces the transaction costs of foreign
investors, who are not familiar with the business environment of some
locations, while incentives more directly increase the rate of return on some
investment projects. Investment incentives fall into two broad classes:
financial incentives and tax incentives (Thomas, 2000). The most common forms
of financial incentives include subsidies and government loans at subsidised
rates. Tax incentives may take the form of general measures to reduce the
corporate tax burden (e.g. through lowering the rates of corporate income tax
or providing tax holidays). Alternatively, countries may offer investment
allowances, accelerated depreciation or tax credits, all of which would promote
capital formation (OECD, 2003).

State aid
rules prohibit aids to undertakings that distort competition and affect trade
between member States unless they meet one of the exceptions. These exceptions
principally deal with equity issues and market failures (e.g. the development
of disadvantaged regions, the promotion of SMEs, R&D, training, employment
and protection of the environment). While the EU-12 countries predominantly
focus on tax reliefs or allowances, the EU-15 countries prioritise innovation
policies to stimulate investment from abroad.

According
to business surveys among foreign investors, financial incentives and grants
are not regarded as primary location factors for multinational enterprises
(UNCTAD, 2011). However, in a number of EU countries,
local authorities often use regional policy grants to attract FDI.[18]
More recently, Basile et al. (2008) found a positive
relationship between FDI inflows and the overall amount of Structural Funds.

Within
the EU, investment promotion activities have proliferated both, in terms of
numbers and in terms of scope (Harding and Javorcik, 2011; Filippov and Costa,
2007). In the EU countries, investment promotion agencies offer a variety of
services, such as practical information and guidance on setting up the business
and assistance in obtaining financial support (grants) from public resources.[19]
Furthermore, generally investment promotion agencies may concentrate activities
on a few priority sectors or target activities. The priority sectors most often
listed are ICT (computer, software and IT services), pharmaceuticals, medical
devices, biotechnology, aerospace, automotive, energy and environmental
technologies. The existence and activities of investment promotion agencies
(IPAs) are expected to have a positive and significant effect on attracting FDI
flows. Harding and Javorcik (2011) show that the effect is only significant for
developing countries, including the EU-10. For high-income countries no
significant relationship has been found. This may indicate that investment
promotion does not work in high income countries where information asymmetries
are relatively low and bureaucratic procedures are less complex.

              4.2.2. Firm-level determinants of FDI

Using
firm-level data enables important observations to be made that cannot be drawn from
aggregate statistics. In this section new evidence is provided on the specific
characteristics of firms and firm-level determinants of FDI decisions is
provided. The theoretical and the empirical literature on multinational
enterprises (MNEs) actively investing abroad suggests that MNEs score better
than non-MNEs on a number of performance indicators. The performance gaps between
MNEs and other firms are born out of the existence of firm-specific assets such
as specific know-how, technology, unique products or intangibles (trademarks,
reputation for quality). In turn, only the most productive firms can pay the
entry costs associated with exporting and FDI and will find it profitable to
engage in foreign production. This idea goes back to Dunning (1977) and
Markusen (2002) and was most recently formalised by Helpman et al. (2004), who
link productivity differences to exporting and FDI and suggest a productivity
ranking with the most productive firms setting up production facilities abroad.
At the same time firms with an intermediate level of productivity choose to
export and the least productive firms neither export nor invest abroad.[20]
The econometric model used here[21]
integrates and tests separately two parts of the FDI decision: the decision
whether or not to invest in a foreign location (the logit part of the model),
and then the decision on the number of affiliates to be set up (the count data
component of the model).

The
evidence on multinational activity in the EU-15 is largely consistent with the
set of predictions drawn from the theoretical MNE literature and from the
earlier empirical findings for individual countries and the euro area. The
analysis reveals that EU-15 multinational firms are larger, employ more capital
per worker, pay higher wages and are more productive than domestic firms and
these firm characteristics are significant determinants of the FDI decision.
This is confirmed by the non-parametric Kolmogorov-Smirnov stochastic dominance
test (not shown) and by the econometric results based on the count data model.[22]

The
analysis also corroborates theoretical results establishing the fact that
foreign direct investment activities are driven by firm-specific advantages and
superior performance in the pre-investment period and that firms self-select
into FDI. Comparing purely domestic firms with investing firms at the beginning
of the investment period, the evidence reveals that they are larger and more
productive, have a larger share of intangible assets, and are more
capital-intensive. Firms that start foreign activities are ex-ante different
from purely domestic firms. Foreign MNEs (multinationals with foreign headquarters)
dominate domestic MNEs in all size and performance indicators except for the
share of intangible assets. This could signal the fact that in the case of
multinational networks, firms still tend to undertake most of their R&D and
related activities in the home country of the headquarters (Dunning and Lundan,
2009).

Results
from the count data model (see Table A.4. in the Appendix) show that the size
and the capital intensity of firms have the strongest effects, while
productivity and the share of intangible assets play a statistically
significant, but quantitatively more limited role in determining the FDI status
of EU-15 firms. The relatively small impact of labour productivity might be due
to (a) the lack of a more detailed distinction among different types of
non-MNEs such as between domestic exporters and domestic non-exporters and (b)
inadequate discrimination between the various types of MNEs. Both reasons might
confound the relationship. Domestic exporters are more productive than
non-exporters; MNEs with only one subsidiary might be more equal to domestic
exporters than MNEs with a higher number of subsidiaries.

The
analysis also finds significant heterogeneity within the group of MNEs.
Multinational firms holding more than one foreign subsidiary outperform all
MNEs with a single subsidiary in terms of size, productivity, capital intensity
and the share of intangible assets. Multinationals holding subsidiaries in more
than one market score better on performance  indicators than multinationals
serving only one foreign market.

Furthermore,
entry costs vary across locations of foreign subsidiaries. First, the analysis
reveals a strong relationship between firm size and location choice. Larger
firms invest in more distant high-income and emerging countries overseas. It
also finds the highest performance premium in terms of productivity and capital
intensity for EU-15 multinational firms setting up affiliates in emerging
regions in Asia and in CEEC. Furthermore, a significant, but lower impact of
capital intensity on the decisions to invest in Eastern Europe has been found.
This might indicate that relative to other host regions, a greater share of
MNEs invest in Eastern European markets for vertical (ʻcost-seekingʼ)
motives.

The
evidence reported in this section also reveals that while MNEs are clearly larger
than domestic firms, the median size of foreign direct investors is found to be
about 60 employees. It is larger in manufacturing (131 employees) than in the
services sectors (35 employees). For first-time foreign direct investors in
2011 (ʻswitching firmsʼ), the median firm size is about 100 employees
in manufacturing and 30 employees in non-manufacturing. Thus, many medium-sized
manufacturing firms and small service firms engage in FDI. Multi-country FDI
strategies and FDI in more distant emerging markets, however, involve mostly
larger manufacturing firms with a median size between 200 employees and 300
employees.

              4.3 Host country effects of
inward FDI in the EU-27

What
are the channels through which FDI stimulates economic growth and productivity?
What are the main factors that influence the magnitude of this effect? Does FDI
contribute to growth? The question should rather address whether and when
foreign-owned companies contribute to more desirable patterns of resource
allocation or industrial restructuring. Policy making sees FDI as positive for
long-term development; however, the impacts of FDI depend on many factors that can
be varied in order to maximise the benefits of foreign investments.

The
aim of this section is to provide a conceptual framework offering a better
understanding of the main factors and channels through which FDI affects
productivity and economic growth. Most importantly, FDI can provide financing
for the acquisition of new plants and equipment, and can be an important
catalyst of economic restructuring. It can also directly transfer technology to
foreign affiliates, as well as indirectly diffuse or ʻspill overʼ
into local economies. While FDI is capable of producing all these effects, this
does not mean that it necessarily does so. Whatever the direct and indirect
impact FDI has on a given host economy, the effects produced will be
conditional upon many factors (Table 4.3). For instance, the nature of FDI
and the reasons why MNEs carry out investments in foreign economies can be very
different (distinguishing between efforts focused on markets, resources,
efficiency, and strategic assets). Furthermore, the scale of the effects of FDI
also depends on the industries targeted by foreign companies e.g. setting up a
retail store vs establishing a business in high-tech manufacturing. Similarly,
the mode of entry of MNEs (greenfield; takeover, merger and acquisition;
minority shares in domestic firms) may exert different impacts on host
economies. Greenfield FDI is linked to setting up a completely new business
establishment in a foreign country, and therefore the impacts on employment,
human capital, productivity and growth might be larger than in the case of a
takeover, where these impacts are generally less pronounced. The impact of FDI
also depends on the development level of the host country, including the
absorptive capacity of local firms, as well as other factors such as the size
of the market, institutional settings or the level of competition.

Table 4.3 - Main
determinants of the magnitude of FDI impact on local firms

Local firm/ economy characteristics || Foreign investor (MNE) characteristics || Other environmental characteristics

Absorptive capacity || Country of origin of the investor || Distance

Technological gap || Entry mode (i.e. M&A versus greenfield) || between local

Exporting markets || Degree of foreign ownership (e.g. wholly owned, JVs) || firm and foreign

Intangible assets/R&D || Industry affiliation (i.e. primary sector, manufacturing, services) || subsidiary

Human capital || High-tech, medium and low-tech industries ||

Size of the local firms || Innovation and training activities ||

Level of competition in the local markets || Investment motives ||

Government assistance, incentives for FDI || Technology-based ownership ||

Technology sourcing ||

Source: Crespo and Fontoura (2007) and Kravtsova (2008).

              4.3.1. Direct effects of
inward FDI

A
distinction can be drawn between direct and indirect effects of FDI. If foreign-controlled
firms achieve higher labour productivity and capital productivity and create
more jobs than  domestic firms, then the direct effects are positive. This is
because MNEs provide a bundle of characteristics in the host countries that are
not necessarily available locally: technologies, brands, management procedures,
market access, and so on.

In a more systematic taxonomy, FDI has the potential to
directly provide:

·
Financial resources, FDI inflows are more
stable, long-termist, and easier to service than commercial debt and portfolio
investment.

·
Technology, MNEs can introduce modern
technologies, some of which are only available through FDI, some through
technology licences. These corporations can stimulate the technical efficiency
of local firms by providing assistance, acting as role models, and intensifying
competition.

·
Market access, MNEs can provide access to export
markets for goods and some services that are already provided in the host
country.

·
Skills and management techniques, MNEs have
worldwide access to individuals with advanced skills and knowledge, which they
can transfer to their foreign affiliates.

·
Good practices (regarding the environment, for
example), MNEs are leading the way in clean technologies and modern environmental
management systems. Some of these can also spill over to host country firms
(see the next section on indirect effects) and other MNEs.

              4.3.1.1. Growth effects of FDI

One
possible approach to measure the direct impact of FDI in the EU countries is to
estimate Barro-type growth regressions based on cross-section data where GDP
per capita growth is a function of initial GDP per capita, average years of
education and the domestic investment ratio. OLS estimates of Barro-type growth
regressions [23]
show that FDI stocks and flows have a direct impact on growth of GDP per capita
with relatively large marginal returns given the factor share of FDI in GDP
(see Table A.5. in the Appendix). Overall, a 1 percentage point increase
in the ratio of FDI inflows to GDP increases the growth rate by 1.5 percentage
points in the EU-12 countries and 1.2 percentage points in the EU-15
countries. The magnitude of the effects indicates that for the EU-12 countries
the increase in FDI inflows between the second half of the 1990s and the second
half of the 2000s by 2 percentage points accounted for 30% of the increase
in the growth rate of GDP per capita (from 1.4% to 5.1% based on unweighted
averages)[24].

              4.3.1.2. Employment share of foreign affiliates in the EU countries

The
direct importance of inward investment can be measured by the share of
employment of foreign affiliates in the host market based on the inward FATS
statistics (i.e. foreign controlled enterprise statistics).[25]
Foreign-controlled companies play a major role in the EU Member States in terms
of employment, value added and turnover.

Based
on NACE rev. 2 for the year 2008 the employment share of foreign affiliates in
manufacturing was 21% (EU-15: 19% and EU-12: 30%). Other industries where the
employment share of foreign-controlled enterprises is significant are the
followings: information and communication (EU-27: 18%;  EU-15: 16% and EU-12:
32%), administrative and support service activities (EU-27: 15%; EU-15: 14% and
EU-12: 22%)  and financial and insurance activities (EU-27: 13%; EU-15: 9% and
EU-12: 68%). The role of foreign multinationals in employment in the EU is smallest
in construction (3%) and real estate activities (4%). Within manufacturing a
very large variation can be observed in the employment share of foreign
affiliates. This is much higher than the average in pharmaceuticals, chemicals,
transport equipment and electrical and optical equipment. At the same time,
textiles and wood are considered as the least FDI-intensive sectors. Almost all
industries in the EU-12 proved to be more reliant on FDI than in the EU-15.

The
employment share of foreign-controlled enterprises in the manufacturing sector
increased in almost all Member States between 1997 and 2007.[26] In terms of
employment multinationals play an important role in the EU-12 (most importantly
in Hungary, the Czech Republic and Slovakia), employing 42-50% of the total
workforce in 2007. Other FDI-intensive countries reach similar levels of
employment share (e.g. Ireland and Belgium). Over a roughly ten-year period the
increasing role of multinationals can be also observed in the Scandinavian and UK
manufacturing sectors. At the same time in southern countries, such as Italy,
Spain and Portugal, the share of total workers employed by foreign manufacturing
multinationals did not change much and remained at a relatively low level.

It
is interesting to compare the change in the share of foreign affiliate
employment in services to that in manufacturing. In the case of non-financial
services and business services, all EU countries for which data are available
show an increase in the employment share of foreign affiliates, with larger
increases than in manufacturing. A high (21-23%) and increasing employment
share of foreign enterprises can be observed for instance in Denmark, Sweden
and Estonia. However, manufacturing is still much more globalised than services
with the exception of information and communication services.

              4.3.1.3.          Value added share of foreign MNEs

Regarding
the manufacturing sector foreign firms’ share of value
added was larger than their share of employment: 28% in the EU-15 countries and
42% in the EU-12 countries. The economic importance of foreign-controlled
enterprises varies significantly across industries. In the EU-15 foreign
affiliates have the highest share of value added in pharmaceuticals (53 %)
followed by paper, chemicals, other transport equipment, computer, electronic and
optical products, basic metals and motor vehicles (see Figure 4.4). These
industries feature either high capital intensity (e.g. paper and metals) or a
high level of innovation and R&D activities (e.g. pharmaceuticals,
computer, electronic and optical products).[27]
Within services, information and communication services have the highest share
of foreign-controlled enterprises (29%), exceeding the degree of
internationalisation of total manufacturing. One reason for the high degree of
internationalisation in terms of FDI in this sector is the rise of ICT. For the
EU-12 there is a similar ranking of industries with respect to foreign
presence.

Figure 4.4 – Share of
value added of foreign affiliates in the EU based on NACE rev. 2

Note: Number
of EU countries for which data are available range between 16 and 21, except
for pharmaceuticals with 10 countries.

Source: WIFO calculations using Eurostat Foreign-controlled enterprises
data (Eurobase).

              4.3.1.4.          Productivity of foreign controlled enterprises

Foreign-controlled
firms exhibit a productivity advantage over domestically owned firms and this
holds true for almost all industries. The ratio of labour productivity between
foreign- controlled and nationally controlled enterprises is highest in
information and communication services, and wholesale and retail trade (see
Table 4.4).

However, productivity differences between foreign-owned
firms and domestic firms should be interpreted with some caution. The
productivity gap between foreign and local firms may also be due to foreign
investors’ cherry-picking of the best
firms.

Table 4.4 - Labour
productivity of foreign-controlled and nationally controlled firms (ʻ000 EUR)

Value added per person employed in 2008 ||

|| EU-12 countries || EU-15 countries

|| For-eign || Domestic || all || ratio || # ind || For-eign || Do-mestic || all || ratio || # of ind

manufacturing || 29 || 17 || 21 || 171 || (10) || 89 || 53 || 60 || 168 || (11)

water supply sewerage, waste || 30 || 23 || 24 || 128 || (6) || 75 || 82 || 81 || 91 || (8)

construction || 35 || 19 || 20 || 182 || (11) || 71 || 55 || 55 || 131 || (11)

wholesale & retail trade; repairs || 32 || 19 || 21 || 167 || (8) || 84 || 37 || 43 || 228 || (10)

transportation & storage || 29 || 22 || 23 || 132 || (7) || 61 || 56 || 57 || 109 || (10)

accommodation & food service || 16 || 13 || 13 || 122 || (8) || 32 || 39 || 38 || 82 || (8)

information & communication || 73 || 36 || 48 || 200 || (9) || 209 || 97 || 115 || 216 || (11)

professional, scientific & tech. act. || 39 || 30 || 31 || 132 || (7) || 83 || 58 || 60 || 143 || (10)

administrative & support service act. || 24 || 16 || 18 || 143 || (8) || 53 || 37 || 39 || 145 || (10)

Note: The ratio is defined as value added per person employed.
Number of countries for which data is available in parenthesis.

Source: WIFO calculations using Eurostat
Foreign-controlled enterprises data (Eurobase).

Recent firm-level studies show that the productivity gap
partly disappears when foreign affiliates and domestically owned multinationals
are compared (Griffith, Redding and Simpson, 2002, 2004; Criscuolo and Martin,
2009). This suggests that multinationality rather than foreign ownership per se
is the main explanation for the higher productivity level of foreign owned
firms as compared to domestic firms.

Empirical
evidence on the direct effects of FDI can be obtained by calculating the
contribution of foreign-controlled enterprises to total labour productivity
growth. Table 4.5 provides evidence on the direct contribution of foreign-controlled
enterprises to real labour productivity growth for the EU manufacturing sector
using the growth accounting framework introduced by Criscuolo (2005). The
results show that foreign affiliates contribute more than proportionally to productivity
growth when compared it with the employment share of foreign affiliates. In the
EU-15 countries foreign-controlled enterprises in the manufacturing sector account
for 54% of total labour productivity growth. The corresponding contribution for
the EU-15 countries is 62%. This is a large effect given that employment share
of foreign-controlled enterprises is 20% in the EU-15 and 29% in the EU-12.
When the direct contribution of foreign-controlled enterprises is decomposed
into the within effect and the between or compositional effect (i.e.
contribution by the increase in the employment share of foreign affiliates in
the host economy), it can be seen that the between effects account for 45% in
the manufacturing sector in EU-15 countries and 55% in EU-12 countries.

Table 4.5 - Contribution of foreign-controlled enterprises to labour productivity
growth in manufacturing

|| || Contribution in percentage points || foreign || between

|| Average annual productivity growth || domestic || foreign || within || between || % || effect

EU-15 || 4.0 || 1.8 || 2.2 || 1.2 || 1.0 || 54 || 45

EU-12 || 10.1 || 3.7 || 6.5 || 2.9 || 3.6 || 62 || 55

Note: The EU-15 countries include Austria, Denmark, Estonia,
Finland, France, Italy, the Netherlands, Portugal, Spain, Sweden and the United
Kingdom. The EU-12 countries include Bulgaria, the Czech Republic, Latvia,
Hungary, Romania, Slovakia and Slovenia. The time spans are 1999-2007 for the
EU-15 countries and 2003-2007 for the EU-12 countries.

Source: WIFO calculations using Eurostat
Foreign-controlled enterprises data, National accounts database (Eurobase) and
the EUKLEMS database.

              4.3.2. Indirect effects of
FDI on productivity and performance

The
unintended indirect impact of FDI on host countries has been already studied
from many points of view, including economic growth and development, employment
and technology transfer.

The
assumption underlying recent policy initiatives to attract FDI is that FDI
inflows upgrade the technological capabilities, skills and competitiveness of
local firms in the host countries. How does FDI contribute to this when MNEs
try to protect their knowledge? What is the empirical evidence that FDI
upgrades the capabilities and competitiveness of host countries?

 It
has been suggested that spillovers from MNEs to local firms (or other MNEs)
represent an important channel for the dissemination of technology and
knowledge. Unintended knowledge and technology transfers from MNEs to local
economies are usually referred to as the indirect effect of FDI.
Figure 4.5 highlights the main channels through which a multinational
corporation can engage in activities that affect a host country. Inward FDI is
only one of the possible business strategies undertaken by MNEs: licensing, trade
and non-equity forms of inter-firm cooperation (e.g. joint ventures) are also
available options. The impact can be direct (on the foreign subsidiary) or
indirect (on domestic firms). In the latter case, the indirect effect is
divided horizontally (intra-industry effect) and vertically (inter-industry).
Finally, the vertical effect can be divided into forward linkages (downstream
domestic customers) and backward linkages (upstream domestic suppliers).

Figure 4.5 - Channels
for technology transfer

Source: WIFO illustration.

At
least four ways can be identified in which knowledge may spill over from
foreign affiliates to other firms in a given host economy.[28]

1. Imitation
and demonstration effects

These can be implemented by
reverse engineering – efforts in which a firm takes a foreign product apart,
analyses it and learns about the technologies. Domestic companies do not need
FDI for this; imports can be sufficient for the purpose. However, it is easier
to imitate and copy – also in terms of managerial and organisational
innovations – if MNEs are located in the country.

2. Foreign
linkage effects

The foreign linkage effect is a
related demonstration effect: through imitation (or sometimes through
collaboration), domestic firms can learn how to export and reach foreign
markets.

3. Movement
of labour and skills acquisition (i.e. mobility)

When an MNE transfers practices
or technology to affiliates, it has to train its employees in the host country
in question. This new managerial and technical knowledge can spill over to host
country firms when employees with these new skills move to other firms or set
up their own businesses. A number of empirical studies suggest that the
movement of workers between firms is the most important mechanism for
technology and knowledge spillovers[29].

4. Competition
– Market interactions

It is argued that the entry of
an MNE (with better technology and managerial practices) into a host country
will force that country’s firms
to use existing technology and resources more efficiently and/or upgrade to
more efficient technologies. However, competitive pressure can force domestic
firms to exit (crowding-out or business-stealing effects) (Dunning, 1993).

Do
these spillovers take place in all countries and industries? According to the
ʻabsorptive capacityʼ literature (Cohen and Levinthal, 1989 and 1990)[30]
and the recent ʻdistance to the frontierʼ literature[31] the
wider a given development gap is, the less likely it is that the host country
or host country firms will have the human capital, physical infrastructure and
distribution networks – therefore­ more generally the absorptive capacity – to
attract advanced FDI.

Absorptive
capacity can be defined as the ability to recognise the value of new external
information, assimilate it, and apply it to commercial ends – a factor critical
to firmsʼ innovative capabilities. This definition has also become a key
concept in the FDI literature, which has extended the notion of absorptive
capacity by relating it to a firmsʼ prior knowledge: the more a local firm
already knows when an MNE enters the market, the more likely it is to be able
to learn from and imitate the MNEʼs knowledge (positive FDI spillovers). In
the context of a given local enterprise, it is the enterpriseʼs absorptive
capacity that enables it to appropriate some of this knowledge.[32]

              4.3.2.1. New empirical evidence on the indirect effects of FDI on
productivity in the EU-27

The
results shown in section 4.5.1 have addressed the direct impacts of foreign
affiliates on productivity growth. However, they do not allow us to infer
whether foreign firms raise overall growth. The aim of this section is to
investigate whether domestic firms benefit from the presence of foreign MNEs in
both the same and customer industries. Knowledge about the magnitude of FDI
spillovers is important because it can help policy makers to maximise the
benefits of FDI for local enterprises and minimises its adverse effects.

In
order to gain a first idea of the relationship between foreign presence and the
performance of the domestic sector a simple scatter plot using aggregate
country-level data is provided. The results show that in EU countries where foreign-controlled
enterprises in the manufacturing sector initially have a large share of
employment (starting in 1999 for most EU-15 countries and 2003 for EU-12 countries)
the growth in the labour productivity of domestically controlled firms in the
manufacturing sector is significantly higher over the period 1999-2007
(alternatively 2003-2007 for the EU-12 countries; Figure 4.6, left-hand
panel). However, employment growth in manufacturing is not significantly correlated
with foreign presence (Figure 4.6, right-hand panel).

Figure 4.6 - Productivity and
employment dynamics in the domestic sector and initial employment share of foreign-controlled
enterprises in manufacturing (EU-27)

Source: WIFO calculations using Eurostat, Foreign-controlled enterprises
data, National accounts database (Eurobase) and the EUKLEMS database.

When
disaggregated data at the one/two-digit level for the manufacturing sector are
used a significant correlation between foreign presence and labour productivity
growth can be observed. This holds true for both the EU-15 and EU-12 countries
for which data are available (see Figure 4.7).

Figure 4.7 - Employment growth and initial employment share of foreign-controlled
enterprises in manufacturing at the one-digit level in EU-15 and EU-12
countries

Source: WIFO calculations using
Eurostat Foreign-controlled enterprises data, National accounts database (Eurobase)
and the EUKLEMS database.

The
inward FATS database has been combined with national accounts data,[33]
which makes it possible to estimate the impact of foreign presence within the
same industry and in customer industries on the performance of domestically
owned firms. For the manufacturing sector
in the EU-15 and EU-12, OLS estimates at the industry
level show that the impact of foreign presence in the same and in customer
(buying) industries in the initial year has a positive impact on the average
annual growth rate of real labour productivity of the domestic sector. In
summary, the presence of both horizontal and vertical backward spillovers from
FDI can be observed.

The
next step is to investigate the impact of the presence of foreign affiliates on
the productivity growth of domestic companies. Since the activity of foreign
firms is unlikely to affect all firms equally, it is interesting to examine,
whether firms characterized by low productivity growth rates benefit from the
presence of MNEs. The interaction term between the backward production linkage
variable and the productivity gap between the domestic and foreign sector is
significant, indicating that the FDI effect through backward linkages increases
with the labour productivity level of the domestic firms to that of foreign
firms. For the EU-15 countries in the manufacturing sector, the magnitude of
the FDI effect is twice as large as in the industries characterised by a small relative
labour productivity gap as compared to those with a large relative productivity
gap (coefficient of 1.17 for a relative productivity level of 1.9 (=90%) as
compared to 1.9 for a productivity of 1.5 (=50%; see Table A.6 in the Appendix).

In
addition, the results based on firm level data for seven EU-12 countries (including
manufacturing and service firms) show strong evidence of productivity
spillovers from backward linkages. However, the FDI effect is highly uneven
across the different types of firms, with insignificant effects for laggards
(e.g. shrinking firms) and newly founded firms. Companies with lower than
average labour productivity growth are unlikely to benefit from the presence of
MNEs, while spillover effects of FDI on highly productive firms in the customer
industries proved to be significant. In particular, the spillover effects
through backward linkages are higher for fast-growing firms when compared with
the total sample. A negative relationship has been found between productivity
growth of domestically owned firms and the presence of foreign firms in the
same industry, indicating negative horizontal spillovers probably due to a
market stealing effect (see Table A.7 in the Appendix). However, the above
results should be interpreted with caution, because limited data may lead to an
aggregation bias. To overcome the limitations, the Community Innovation Survey
(CIS) is used in the next section to investigate the impact of foreign MNEs on
local firms.

              4.3.2.2. New empirical evidence on the indirect effects of FDI on
employment growth and technological innovations in the EU-10

The
findings of the empirical analysis in this chapter so far have strongly
supported the view that backward spillovers are more important than horizontal
spillovers with regard to productivity growth. However, an open question
remains as to what extent the magnitude of FDI spillovers depends on local firm
characteristics and absorptive capacity. The entry of multinational enterprises
may not only have an impact on productivity and employment growth but may also induce
local firms to introduce new products and/or services or new production
processes. This part of the analysis investigates the impact of FDI on the
employment performance and innovation activities of domestically owned companies
based on CIS 2006 data for eight EU-10 countries.[34] Particular attention
is paid to the role of spillovers from downstream multinational enterprises on
upstream local suppliers (backward linkages).

Special
emphasis is put on the question of the absorptive capacity of local firms and
firm characteristics (e.g. firm size). The analysis is based on a large firm
sample, namely the CIS 2006 for eight EU-10 countries with about 36000 observations.
This analysis focuses on the EU-10 countries.[35] The reason is that the
productivity differences between domestically and foreign-owned firms are much
more pronounced in the EU-10 countries than in the EU-15 countries.

The
major contribution of this analysis is that it investigates the relationship
between the employment performance of local firms and FDI along with the impact
of FDI on the innovativeness of local companies. Few studies have investigated
the impact of foreign presence on technological innovation in
domestically-owned firms[36].
Using data for 27 countries in Central and Eastern Europe (including the EU-10
countries), Gorodnichenko et al. (2010) find that domestic firmsʼ
innovation activities increase through backward linkages by supplying
multinational enterprises.

OLS
estimates (see Table A.8 in the Appendix) based on eight EU-10 countries show
that foreign presence has a positive impact on employment growth of firms
located in local supply industries. In particular, local firms with backward
linkages in industries with a large initial foreign employment share have a
significantly higher average employment growth rate in the next two years. In
other words, local firms with a larger supply of inputs to industries where
foreign firms are present tend to create more jobs than industries with no such
linkages. The magnitude of the spillover effect through backward linkages
increases with the absorptive capacity of local firms measured as the initial
productivity level of domestic firms to that of foreign firms. However, the
additional effect of the increased absorptive capacity is relatively modest.

Furthermore,
foreign competition leads to a higher probability that local firms will
introduce new product innovations where foreign competition is measured as a
subjective qualitative indicator as perceived by local firms. A new empirical
finding is that the magnitude of the impact of FDI through backward linkages
increases for innovative local firms (i.e. firms that introduce new products
and/or new services) in the manufacturing sector.  Overall, the results show
strong evidence in support of vertical spillovers through backward linkages
from foreign buyers to local suppliers. Local firm characteristics also
influence the strength of FDI spillovers. Spillovers through backward linkages
to local firms are present for local firms in the manufacturing sector and
generally for firms with 25 or more employees but do not exist for small firms
with less than 25 employees and for domestically owned firms in the service
sector. Moreover and somewhat unexpectedly, the results show that spillovers
through backward linkages to local firms are much larger for non-exporting
firms than for exporting firms. There is also evidence that firms in the same
industry benefit from industry-level FDI that increases with absorptive
capacity. However, the magnitude of the effects is much smaller than that of
spillovers through backward linkages.

The
relationship between foreign presence and the innovation performance of local
firms is also investigated (Table A.9 in the Appendix). The results show a
positive association between innovation performance of domestically owned firms
and foreign presence in customer industries. This suggests that local firms in
industries that supply a larger share of their output to industries with a larger
share of multinational enterprises are more likely to introduce product
innovations or new market products. However, the positive effect only occurs
when the productivity gap is not too wide and increases with the relative
labour productivity level between local and foreign-owned firms. Furthermore,
the positive impact of FDI can be observed in all kinds of innovation
activities (i.e. new market products, product and process innovations[37]) but it is the
largest for product innovations. Hence, FDI favours technology adoption (i.e.
goods and services that are new to the firm) rather than radical innovations
(i.e. market novelties).

Overall,
the results suggest that foreign firms act as catalysts for domestic suppliers
to introduce technological innovations in the case of EU-10 countries. In
addition, foreign firms do not crowd out domestic innovation in the same
industry and there are positive effects with increased absorptive capacity. An
important result is that not only do domestic suppliers benefit in their
innovation performance from the presence of multinational enterprises, but
technological innovations of local firms and that of foreign firms are also
significantly positively correlated. In other words, the introduction of
technological innovations by domestic and foreign firms goes hand in hand
(holding everything else constant and accounting for industry effects). [38]

              4.3.2.3. Evidence for technology transfer through backward linkages and
the use of technology licences

The
aim of this section is to analyse the characteristics of local firms that
supply goods and services to multinational enterprises. It also examines to
what extent foreign affiliates contribute to technology transfers in the form
of technology licences.

There
are a number of reasons why multinationals prefer local procurement rather than
suppliers from abroad. Geographical proximity can lower production costs and
makes face-to- face contacts easier, and close relationships with local
suppliers make it easier to tailor products and services to local market
conditions. However, in some industries local sourcing is less frequent because
multinational companies prefer to work with their established suppliers
(UNCTAD, 2001, 2003). The factor determining the supply status of supplies MNEs
is estimated using a probit model. Information on the level of use of local
suppliers by foreign firms also makes it possible to estimate an ordered probit
model.[39]

In
the EU-10 in 2004, 17% of local firms supplied goods or services to foreign
affiliates located in the same country (not including the parent company) (see Table
4.6). This share is higher than the average in the case of transport services
(24%), mining (23%), manufacturing firms (19%), and business services (19%).
Most of the local firms have a low share of goods and services supplied to
MNEs. Furthermore, the supplier status and the share of sales increase with
firm size. Overall, the incidence of supplier linkages between local and
multinational firms is quite significant given the practice of multinational
enterprises of purchasing from established suppliers.

Table 4.6 – Share of
domestic sales to multinational enterprises and their foreign affiliates by
local firms in 2004 by industries, EU-10

Share of domestic sales to multinational enterprises in host country of local firms

|| 0 || 1-24 || 25-49 || 50-74 || 75-100 || total || 1-100

|| || || by industry

mining || 77 || 9 || 5 || 9 || 0 || 100 || 23

construction || 86 || 7 || 4 || 1 || 1 || 100 || 14

manufacturing || 81 || 9 || 4 || 3 || 3 || 100 || 19

transport || 76 || 11 || 7 || 4 || 2 || 100 || 24

trade || 87 || 9 || 2 || 1 || 1 || 100 || 13

real estate, renting, business serv. || 81 || 11 || 2 || 3 || 2 || 100 || 19

hotel and restaurants || 87 || 8 || 4 || 0 || 0 || 100 || 13

other services || 90 || 7 || 1 || 1 || 1 || 100 || 10

total || 83 || 9 || 3 || 2 || 2 || 100 || 17

|| || || by size

firm size || || || || || || ||

>5 || 93 || 4 || 1 || 2 || 1 || 100 || 7

5 - 24.9 || 85 || 9 || 3 || 2 || 1 || 100 || 15

25-49.9 || 78 || 12 || 5 || 3 || 2 || 100 || 22

>=50 || 79 || 11 || 5 || 3 || 2 || 100 || 21

total || 85 || 8 || 3 || 2 || 1 || 100 || 15

Note: Figures
are based on the question ʻWhat percentage of your domestic sales are to
multinationals located in your country (not including your parent company, if
applicable)?ʼ using 3500 firm observations.

Source: BEEPS 2005.

Unreported
results show that firms with new products are more likely to become a supplier
to multinational enterprises in the same country. Innovative firms have a
7 percentage points higher probability of being a supplier than
non-innovative firms. Local firms in construction, wholesale and retail trade,
and hotels and restaurants have a lower likelihood of being a supplier to
multinational enterprises. As expected, firm size has a positive impact on
being a supplier to MNEs, with the probability decreasing slightly with
increased firm size. Furthermore, the skill structure is of great importance in
being a supplier to foreign affiliates: firms with a larger share of workers
with some or completed university education have a significantly higher
probability of being a supplier to MNEs.

The next step is to investigate the extent of technology
transfers from foreign-owned firms to local firms in the form of technology
licences. In particular, it is examined to what extent foreign affiliates
contribute to technology transfer and help to upgrade local suppliers in the
host economy with respect to innovation performance and innovation input. The
focus is on externalised technology transfer, i.e. linkages and transfers
outside direct transfers such as licences, franchises or subcontracting
(Ivarsson and Alvstam, 2005). These
types of technology transfers have the potential to contribute to technology
upgrading (UNCTAD, 1999).

Figure 4.8
shows the share of firms that use technology licensed from foreign-owned
enterprises in the manufacturing sector in the EU-10. About 15% of the firms
use licences from foreign-owned firms with large differences across the EU-10
countries.

Figure 4.8 - Use of
technology licensed from a foreign-owned company, excluding office software,
manufacturing in 2008, in %

Note: Weighted using sample weights.

Source: BEEPS 2009 based on 1100 observations.

As
expected, firms that use technology licences are more likely to introduce new
products and product innovations and to undertake R&D. In the manufacturing sector 63% of local firms having
licences with foreign MNEs engaged in product innovation in 2008.  At the same
time only 51% of local companies without technology licences proved to be
innovative. The percentage of firms with R&D activities is 40% for firms
with licences and 21% for those with no licences. This may indicate that the
use of licences from foreign-owned companies leads to technological upgrading
of local firms but may also indicate that innovative firms and R&D-intensive
firms are more likely to use technology licences.

              4.4 Trends and structures
of EU-27 outward FDI

At
global level, the EU is the largest direct investor, typically accounting for
more than half of global FDI outflows (intra-EU flows included). In line with
the global trend, the investment activity of EU MNEs decreased substantially
and resulted in the EUʼs share of global outflows dropping to a third in
the years 2009 and 2010.

Both
extra-EU and intra-EU outflows contracted in absolute terms after 2007 and did
not return to the peak levels of 2006 and 2007 until 2010. EU MNEs curtailed
FDI activities  particularly within the EU, which is reflected in a marked
decline in intra-EU flows since the peak in 2007 (Figure 4.9). Intra-EU
outflows dropped by almost 40% in 2008 and again by 50% in 2009 to around
EUR 140 bn and stabilised at that level in 2010.

Outward
FDI flows to countries outside the EU also contracted and were down for the
third consecutive year in 2010 shrinking to EUR 143 bn, less than
half of their peak value in 2007. Despite their severe 40% decline in 2009
extra-EU flows have gained relative importance since the crisis. Between 2008
and 2010 the share of extra-EU outflows hovered around 50%. The number and
value of EU greenfield investments went down and the average size of projects
was typically smaller in the period 2009-2011.

Figure 4.9 - EU FDI outflows, 2001-2010 (EUR bn)

Note: EU
is EU-25 for 2001-2003 and EU-27 for 2004-2010. EU flows calculated as the sum
of EU Member States. Intra-EU flows to Luxembourg are adjusted downwards by 90%
in order to exclude activities of Special Purpose Enterprises (SPEs). Extra-EU
flows exclude offshore centres (Guernsey, Jersey, Isle of Man, Gibraltar,
Bahamas, Bermuda, British Virgin Islands, Cayman Islands, Netherlands
Antilles).
Source: Eurostat, wiiw
calculations.

The
shift in outward FDI from intra-EU to extra-EU flows might indicate that EU
MNEs have perceived the EU as a less attractive location for FDI since 2008,
inducing several European MNEs to seek investment opportunities in fast-growing
emerging markets outside the EU. Another factor contributing to the shift in
the destinations of FDI is that until mid-2008 the EU-10 countries provided
excellent investment opportunities for EU MNEs, but the convergence process was
interrupted by the economic crises of 2008/2009 and these countries stopped
being a focus destination for EU MNEs.

              4.4.1. EU outward FDI by
destinations: a shift towards emerging markets

Like
the main sources of the EUʼs inward FDI from the rest of the world, the
main recipients of EU outward FDI are the US and the EFTA countries. These two
regions accounted for more than half of the total extra-EU outflows in the
period 2008-2010. This supports the view that the dominant share of EU FDI is
market-seeking FDI targeted at high-income economies. However, as a result of
the crisis, investment by EU MNEs in developed destinations – with the
exception of Switzerland - declined significantly. This is partly linked to the
recession in developed countries and the dominant role of M&As between
developed countries, which are more sensitive to business fluctuations than greenfield
investments.

At
the same time emerging economies, mainly in Asia and South America have clearly
become more important destinations for EU FDI. This trend had started well in
advance of the economic crisis of 2008/2009 but the European recession
intensified it. In 2008-2010, 11 out of the 15 largest FDI destinations were emerging
and transition economies, including Russia, Brazil, Mexico, China, Turkey and
India. Developing regions bordering the EU benefited to a lesser extent from EU
FDI, with the notable exception of North Africa (see more about this in Chapter
6). In general, flows to emerging countries were much more resilient to the
crisis. This is due to the fact that these markets have higher growth
performance and prospects and are thus ideal targets for greenfield
investments.

EU
MNEs account for a significant share of overall FDI stocks in major destination
countries. The overwhelming majority of the EU FDI stock in non-EU countries is
owned by companies from the EU-15 (97%) while the EU-12 accounted for about 3%
in 2010.[40]
EU multinationals are particularly well positioned in the US, Switzerland,
Russia and Argentina[41]
accounting for 64%, 71%, 83% and 55%, respectively, of the total FDI stock in
the country. EU companies represent a much larger share of inward FDI stocks in
many countries than US or Japanese competitors, indicating a good competitive
position in foreign markets. For instance, in both India and Argentina, the EUʼs
share of the FDI stock is two and three times larger than that of the US. Only in
China, EU firms seem to be on a par with the US in terms of accumulated FDI
stocks. China seems to be a particularly competitive market for foreign direct
investors as there is strong competition there also from South Korea and
Singapore.

               4.4.2.            Industry
structure of the EU outward FDI: the EU possesses comparative advantages for
FDI in manufacturing industries

Like
FDI in general, EU outward FDI by broad economic sectors takes place
predominantly in services. Services emerge as the main sector accounting for
72% of the total outward FDI of the EU, while manufacturing represents 20%.
These figures are biased towards the services sector due to the massive FDI
stocks of the financial sector. However, excluding the financial sector and the
activities of holding companies (other business services), the services
industries account for 29% of total EU outward stocks. Most investments in this
sector target the trade and repair industry (10%) and the post and
telecommunications industry (7.4%). Manufacturing industries account for half
of the total (adjusted) EU outward stocks in non-EU countries amounting to
EUR 645 bn. The chemical industry (14%) is the leading industry in
terms of EU outward FDI stocks owned in the rest of the world, followed by the
metal industry (6%) and the food industry (6%). Generally speaking, the
magnitude of the EU outward stocks in the individual industries reflects the
strong competitive positions of the EU companies in the respective industries.
The variation across destinations markets shows that host country factors,
including resource endowments and the importance of the industry in the host
economy, also play a role in investment decisions of EU firms. For instance,
the EU and Switzerland both have large multinationals in the chemical industry,
and a large share (43%) of EU total outward FDI stock in the chemical sector is
located in Switzerland. Another example is the low presence of EU (and other) multinationals
in the Indian market in the trade and repair industry, which is a clear
consequence of the prohibition of the FDI in multibrand retailing.

In
the analysis of trade flows it has become common to investigate the relative
position of a country in a specific industry by looking at revealed comparative
advantages (RCAs). Basically, RCAs signal the industries in which a given
country exports relatively more than it imports in comparison to the export and
import ratio in the total economy. EU outward FDI stocks by industries are used
to apply the concept of RCAs to FDI stocks by comparing inward with outward
stocks. Calculating RCAs based on inward and outward EU FDI stocks suggests
that EU MNEs are competitive in manufacturing industries, including the EUʼs
traditional industry strongholds (i.e. chemicals, machinery, vehicles) see
Figure A.1.in the Appendix. The EUʼs RCAs in both manufacturing industries
and the mining and quarrying sector are based on technological capacities. In
manufacturing, this conclusion is derived from the fact that the EU enjoys RCAs
mainly in relatively more technology-intensive industries. In mining and
quarrying EU MNEs seem to have developed technologies that allow them to
exploit natural resources abroad despite the EUʼs relative resource
scarcity. In contrast in services industries, including knowledge-intensive
industries such as R&D and computer activities, revealed comparative
disadvantages have been found. This suggests that EU MNEs in these sectors are
less competitive than foreign MNEs.

              4.4.3. The importance of EU
MNEs in the EU-15 countries

Looking
beyond the major developments in FDI outflows at the aggregate and sector
level, the analysis at the firm level provides additional insights into the
number of multinational firms and their importance for the EU. Due to data
limitations the sample is restricted to EU-15 firms.[42]
The empirical literature suggests that foreign MNEs are more productive, more
capital-intensive, larger and pay higher wages than firms operating exclusively
in the domestic market. Furthermore, only a very small fraction of EU-15 firms
own foreign affiliates, but they account for a disproportionately large share
of domestic activity. The share of MNEs is typically larger in small countries.
The share of domestic MNEs is larger than that of foreign MNEs in all EU-15
countries except for Luxembourg.

Despite
their small share in total number of firms (2.8%), MNEs (domestic and foreign
MNEs together) account for 21.1% of employment, 28.1% of turnover, 37.2% of
total fixed assets and 36% of intangible assets in the EU-15. Domestic
multinational enterprises – domestic to each individual country in the EU-15 –
account for the largest share of these activities, while foreign multinational
enterprises account for a much smaller proportion (Figure 4.10.).

 Figure 4.10 - Contribution of EU-15 multinational
enterprises to domestic activities

Source: AMADEUS database (2011
release), WIFO calculations.

Multinational
firms that own subsidiaries in more than one foreign country account for a mere
1% of the total number of firms in the sample, but generate 15% of employment,
20% of turnover and 27% of total fixed assets and intangible assets. Roughly
the same picture emerges for multinationals that own more than four foreign
subsidiaries. This is an indication that these MNEs are on average larger
firms.

The
international activity of multinational firms is quite concentrated. The largest
25% of MNEs account for almost 30% of the total number of foreign subsidiaries,
76% of total turnover and intangible assets and  generate 90% of employment.
However, they represent only 15% of the total number of MNEs in the sample.

Activities
of EU-15 MNEs are highly concentrated in the EU. The firm-level data reveal
that 70% of EU MNEs choose the EU-15 and 45% choose locations within the EU-15
exclusively. The top three destinations in the EU-15 are Germany, the UK and
France. Regarding non-EU countries most European firms prefer to operate in the
US market. MNEs in the service sector tend to invest more outside the EU than
manufacturing firms. First-time investors prefer closer locations in Western
and Eastern Europe. Furthermore, almost half of the new investors place their
initial investment in the EU-15 and 15% in the EU-12 and only a very few first-time
investors operate affiliates outside Europe.

Most
MNEs own only a small number of foreign subsidiaries, and are active in a small
number of different host countries. More than half of MNEs hold only one subsidiary,
and nearly 60% of the MNEs are active in only one foreign market.

In
terms of location choice, the analysis reveals weak evidence of a sequence of
markets, in the sense that on average MNEs tend to set up affiliates in less
popular markets only if they already have a subsidiary in one of the more
popular markets.

              4.4.4. Emerging outward FDI from the new EU Member States (EU-12)

The
trends in overall EU outward FDI reflect mostly the pattern of EU-15 countries.
Linked to their high GDP per capita level, as expected, most of these countries
are net capital exporters, with outward FDI stocks exceeding inward FDI stocks.
The new EU Member States (EU-12) in turn have been clearly the focus of inward
FDI over the past decade. Foreign MNEs made a significant contribution to structural
change and development. While EU-12 countries were the source of very low
levels of outward FDI, there are several signs that FDI outflows and outward
FDI positions are gradually catching up. In line with the theoretical notion of
the ʻinvestment development pathʼ[43]
(Dunning, 1981, 1986), there has been a growing number of ʻemerging
multinationalsʼ operating from the EU-12. FDI outflows from these
countries increased from around EUR 4 bn in 2003 to
EUR 7.5 bn in 2010 and peaked at levels of up to EUR 14 bn
in some of the pre-crisis years (Figure 4.11).

Figure 4.11 - EU-12 FDI outflows, 2003-2010

Source: Eurostat.

The
total stock of capital invested abroad by EU-12 countries reached
EUR 81.8 bn in 2010, having increased nearly sevenfold from its 2003
value. As a result, these countries almost tripled their share in total EU
outward FDI, from 1.3% in 2003 to about 1.8 % in 2010. Moreover, the EU-12
outward FDI stock grew also in relation to the inward FDI stock in these
countries: from 7.2% in 2003 to over 16% in 2010 (Figure 4.12). This growth
occurred despite a more than threefold increase in the value of inward FDI
stock in these countries: from EUR 167 bn in 2003 to
EUR 507 bn in 2010.[44]

Figure 4.12 - Inward
and outward FDI stock (EU-12, 2003-2010)

Source: WERI calculations based on Eurostat.

In
line with the general downturn in outward FDI activities during the crisis,
activities in the EU-12 also slowed down. However, this does not indicate a
change in the overall trend of an increasing outward flows from the region. The
decline was not steep and the value of outflow investments from the region in
2009-2010 was still significantly higher than in 2003-2005.

In
most years greenfield FDI projects outweigh M&A deals in numbers (Figure 4.13).
The crisis-related fall in M&A was steeper than that in greenfield investments
and the average size of investment projects has declined since the crisis, for
both types of investment projects, but much more so for M&A deals than for greenfield
investments. While greenfield investments recovered in 2010, the number and the
value of M&A continued to decline.

Figure 4.13 -
Greenfield FDI projects and M&A deals by MNEs from EU-12 (number of deals
and value in EUR bn)

 Source: WERI calculations based on the fDi markets database.

Regarding
individual countries, Poland, the biggest economy in the EU-12, held a 35.7%
share of the value of the total outward FDI stock from the region. Hungary was
the second largest investor from the EU-12 region (18.0%), followed by the Czech
Republic (13.3%). However, relative to GDP, smaller countries such as Estonia,
Slovenia and Hungary are the best performers in terms of internationalisation
through outward FDI.

While
in the pre-accession period FDI outflows from the EU-12 were strongly
concentrated in regions outside the EU-27, this changed to a much stronger
focus on intra-EU flows after  accession. In 2010 well over 50% of the total
EU-12 stock of outward FDI constituted intra-EU-27 investments (see
Figure 4.11). Note that this is a different trend to the one that has been
found inherently for the EU-15 in the analysis of overall EU foreign direct
investment trends.

Distinguishing
between the types of outward FDI projects, the geography of M&A is highly
influenced by ʻround-trippingʼ FDI deals, referring to investments
that are channelled back to the original investing country by Special Purpose
Entities (holding companies) located in financial centres or tax havens. This trend
is mostly reflected in foreign direct investments in Cyprus, the Netherlands,
the UK, Switzerland and Luxembourg. Another clean dominant trend is for M&A
deals in proximate, neighbouring countries within the Central-East European
region. The largest EU-15 locations for EU-12 M&A activities are Germany,
Austria and Italy, while Romania, Lithuania, the Czech Republic, Bulgaria and
Slovenia are the main destinations within the EU-12. Extra-EU M&As are most
intensively undertaken in neighbouring Croatia, the Ukraine, Serbia and Russia.

The
geography of greenfield FDI is less influenced by factors related to financial flows
resulting from tax optimisation. The main focus is on countries within the EU-12
region itself – foremost Romania, the Slovak Republic and Bulgaria – and
neighbouring countries in Eastern Europe (Russia and Ukraine) along with
markets of the former Yugoslavia in South-Eastern Europe. The most important
target countries for greenfield investments from the EU-12 are Germany, Italy,
the UK and Austria. It is worth noting that some outward investment is oriented
toward emerging regions in Asia.

The
main feature of the sector structure in the EU-12 is a very strong focus on
construction and engineering and on the coke and refined petroleum products.
Comparable to the overall EU sector pattern of outward FDI, the investment
activity of EU-12 MNEs is dominated by the service sector. The total value  of
manufacturing projects is greater than that of greenfield projects. Apart from
finance and insurance which leads in M&A projects, the focus of FDI from
the EU-12 is on transportation and wholesale and retail trade.

              4.5 Home country effects of outward FDI on EU industry

A
debate is ongoing in most developed countries about the possible adverse
effects of outward FDI on domestic industries. In particular, the fear of
job-exporting has sparked widespread concerns due to the increasing
attractiveness of emerging and fast-growing and low-wage countries. This is a
highly controversial issue in the EU-15 Member States, which see themselves as
affected by such concerns, especially since the eastern EU enlargements in 2004
and 2007 and the intra-EU reallocation. A related issue is the increase in the
internationalisation of corporate R&D and fears that the offshoring of
R&D activities of multinational enterprises is hollowing out the innovation
base in the home country. On the other hand, outward FDI is seen as a means to
gain market access and secure market shares, to reduce production costs and
gain access to technologies and know-how of foreign countries, with positive
feedback to the growth and the international competitiveness of home-based
parent companies. Moreover, as reviewed in section 4.3.4 multinational firms
are found to be more productive, larger and more capital- and technology-intensive,
to pay higher wages and to employ a more highly skilled labour force. For all
these reasons, countries with an increasing share of multinational firms should
experience an increase in aggregate productivity and aggregate competitiveness
on international markets.

The
theoretical predictions on the home-market effects of outward FDI are far from
clear-cut and depend on the type of motive for outward foreign direct
investments and the very specific relationships between the parent company and
its foreign affiliates. The main questions that are raised in terms of direct
effects typically treat FDI as an exogenous event and then seek to examine the
impact on performance or employment. This is highly dependent on the motivation
of the firm, home country characteristics and the industry in which FDI takes
place.

The
motivation of the firm to undertake FDI influences both the scale and scope and
also the level and destination of FDI. In turn, these factors will also lead to
very different impacts at home (Buckley and Casson, 2009; Driffield et al.,
2009; Driffield and Love, 2007). Table 4.7 provides a synopsis of the impacts
of the different types of FDI, based on the existing literature, in terms of
the effects on employment, skill structures, technology transfer, productivity
and profitability.

Table 4.7
– Home-market effects of outward FDI depend on the motive for going abroad

Typology || Motivation || Employment || Technology transfer || Productivity || Skills || Profitability

market seeking || the desire to exploit existing firm-specific assets in new markets || little reallocation, some expansion at home, may also replace exports || technology is exported || neutral || potential increase for skilled labour at home to coordinate new activity || positive

resource seeking || the desire to access (natural) resources abroad || positive || neutral || neutral || neutral || positive

efficiency seeking || (re)location of activity to low-cost locations || negative for low-skilled workers and positive for high-skilled workers || neutral || potentially positive on average as more productive activities are retained at home || home- country activities become more skill- intensive, as  demand for low-skilled workers is reduced at home || positive

technology sourcing || the desire to access new technology abroad || may be positive in the long run || positive || positive || increased demand for skilled workers at home || positive, but only in long run

Source: WIFO illustration.

The
background study provides an overview of the empirical literature reviewed.
While it is possible to draw feasible conclusions on the impact of FDI from this
review with respect to productivity, profitability and technology transfers,
there remain some areas where the home -country effects remain uncertain. These
mostly relate to employment effects, where the literature presents a very
heterogeneous picture.

              4.5.1. Employment effects

The
most pressing question in terms of the employment effects of outward FDI is the
extent to which it leads to a reduction in employment at home. A glance at the literature
on home country employment effects in the background study (Falk et al. 2012) shows
that European firms that have engaged in FDI in low-cost locations are more
likely to decrease the demand for low skill worker and increase the demand for
high skill workers with an overall ambiguous effect. However, this represents
only about a third of the total FDI by EU firms, with FDI in general producing
more positive impacts on employment. Even where outward FDI does lead to a
reduction in employment, the ʻemployment substitutionʼ is much less
than 100%.

When it
is possible to differentiate between motivations and locations, it has been
typically found that a doubling of FDI to low-cost locations reduces the demand
for unskilled workers by some 4%, while it leads to a similar increase in the
demand for skilled workers, (Driffield et al., 2009). The findings of
Copenhagen Economics (2010) suggest that EU outward FDI has had no measurable
impact on employment at the aggregate level. However, bearing in mind the very
different data sets and estimation techniques that are used, and the different
measures of FDI (from employment abroad to capital flows, and even assets held
abroad), it is impossible to draw strong conclusions about the employment
effects of outward FDI.

              4.5.2. Skill structure

In
recent years both academics and policy makers have expressed concern that
increasing globalisation, in the form of both foreign direct investment (FDI)
and international trade, is causing dramatic changes in labour demand in the
developed world. Specifically, that demand for unskilled workers in the US and
Western Europe has been declining and will continue to decline as unskilled
workers face significant competition from the newly industrialised countries
and other parts of the developing world.

One
of the biggest problems when seeking to examine the impact of FDI on skill
structures in Europe, and to arrive at any clear conclusions, is that labour
market flexibility differs greatly even within the EU-15 countries, and has
changed over time. In general, labour market flexibility rewards more skilled
workers, who not only have higher earnings but more secure employment. Outward
FDI enhances this, rewarding more skilled workers while relocating low-skill
activities elsewhere.

Empirical
work on the impact of outward FDI on relative employment of different skill
levels is limited in scope. A central aspect of the relevant literature is the
difficulty of separating the effects of outward FDI from that of skill-biased
technological change. The introduction of new technologies and the decision to
offshore production activities or services often occurs simultaneously, making
it difficult to isolate the effects. This literature can be summarised by two
key points. The first is that where the home country has a technological
advantage and where this is reinforced by lower unit labour costs then outward
FDI increases the demand for skilled labour. Secondly, the higher level of
skills an individual has, the better placed they are to gain from FDI in either
direction.

              4.5.3. Technology transfer

Benefits
from knowledge flows between MNE parent companies and their affiliates abroad
are most likely in cases where strategic knowledge and technology sourcing are
the key motive for FDI, especially between advanced economies. Recent evidence
suggests that corporations are increasingly moving their R&D facilities
abroad. This is being done as part of a strategic move away from merely
adapting ʻcoreʼ technology to a foreign market towards a much more
central role in product innovation and development. Companies which previously
exerted rather tight control over their R&D sites are now granting more
autonomy and empowerment to R&D laboratories situated abroad. Since the
1990s organisations have begun to take a more decentralised approach to R&D
(Pearce, 1999; Niosi, 1999). In addition, the literature suggests that there is
a growing willingness to locate such facilities close to leading centres of
research and innovation specifically with a view to absorbing learning
spillovers from geographical proximity to such sites (Serapio and Dalton, 1999;
Ito and Wakasugi, 2007).

The
existing empirical studies also provide evidence on extensive ʻreverseʼ
knowledge flows from affiliates to parents. This indicates that
knowledge-sourcing is indeed an important determinant of outward FDI. However,
these flows might not always spill over to the home economy. On the other hand,
outward FDI, without any intra-firm knowledge transfers, creates spillovers of
knowledge back to the home country. Thus, intra-firm knowledge transfers are
neither necessary nor sufficient for subsequent spillovers to the home economy.
However, the fact remains that spillovers are overwhelmingly more likely to
occur where there exists parent-affiliate knowledge transfer exists.

              4.5.4. Productivity

In line
with the evidence reported on the characteristics of EU-15 MNEs, the bulk of
the empirical literature on FDI and productivity finds that firms self-select
into foreign markets, via either exports or FDI. This self-selection means that
they are already performing better than the rest of the population of firms. These
companies are more productive than average, sometimes as much as 25% more
productive than the rest of the firms. However, there is additional evidence suggesting
that there is a positive productivity gain associated with increased outward
FDI, which in turn depends on the type of investment undertaken.

Typically,
the main theoretical rationale for the home country to expect benefits from
outward FDI is based on the likely indirect effects (Driffield et al., 2009).
As firms locate abroad, they may improve their overall performance and
efficiency by relocating only low value-added production abroad and keeping and
even expanding high value-added activities at home. The standard analysis
suggests that such FDI flows merely reflect the desire to locate in the lowest
possible cost locations. FDI of this type may well generate productivity growth
at home, through what Blomström and Kokko (1998) highlight as the ‘batting
average’ effect of outward FDI that can occur as a result of the reallocation
of resources that may accompany FDI, especially to low-cost locations.

Positive
feedbacks from FDI to productivity at home are also associated with successful
technology and knowledge sourcing and benefits from agglomeration effects in
specific sectors (Barba Navaretti and Venables, 2004), or effects related to
the general notion of ʻlearning by exportingʼ due to exposure to
international competition, best practice and the technology frontier as well as
demonstration effects (Clerides et al., 1998).

              4.5.5. Profitability

Much of
the literature concerning the relationship between outward FDI and
profitability centres on what has become known as the multinationality-performance
debate. Overall, the literature finds that multinationals are more profitable
than others, but with some evidence that this is because the more successful
firms become multinational. However, overall multinationality is associated
with long-run profitability. One weakness in this literature is that it
typically fails to distinguish between either the location of the FDI or its
type. For example, Driffield and Yong (2012) find that FDI from EU firms to
developing countries is more profitable (though less productive) than FDI
between EU countries.

The importance of mergers and acquisition (M&A) activity
also has to be considered in this regard. Gugler et al. (2003) analyse the
effects of M&A activity around the world for a 15- year period. They
separate the effects of domestic and cross-border M&A on firms’ profits and
market shares and show that mergers on average do result in significant
increases in profits, but reduce the sales of the merging firms. Differences
between mergers in the manufacturing and the service sectors, and between
domestic and cross-border mergers are also found to be minimal.

              4.6 Conclusions and policy implications

Impacts
and motivation for FDI policies. Investment
in its various forms is generally acknowledged to be the main driver of
economic growth, without ever giving rise to much controversy about its
desirability. In contrast, due to its transnational character, FDI conducted by
multinational enterprises demands additional attention. It is important to
continue designing smart policies to encourage more and responsive FDI, while
applying the principle of Policy Coherence for Development. On the one hand,
economies aim to attract inward FDI, counting on its direct contribution to the
job creation and productivity growth and anticipating of positive indirect
effects through knowledge spillovers and user-supplier linkages. This applies
in particular to greenfield investments, whereas M&As are sometimes viewed
with reservations in the host country. On the other hand, outward FDI is often
considered a sign of economic strength, e.g. by securing competitive assets or
opening markets abroad. Again, the positive attitude towards internationalisation
does not always predominate, for example when there is a fear that domestic jobs
will be offshored to lower- cost locations.

This
chapter has reviewed the literature and provided new empirical evidence on the
trends, determinants and impacts of FDI. Overall,
the evidence confirms the general view that FDI inflows into the EU have a
direct and significant effect on economic growth and productivity growth in the
host country. And the marginal contribution of foreign investment appears to be
greater than the growth stimulus of an equivalent amount of domestic
investment. Greenfield investment especially not only brings new capital, but
often creates employment both directly in the affiliate and indirectly through
supplier linkages to local firms.

The
review of the home country effects of outward FDI also shows the effects on 
productivity in the home economy are predominantly positive. The evidence in
the literature on the impact on employment is less clear. When employment
substitution takes place, it is mostly to the detriment of low-skilled workers,
but it is difficult to disentangle the impact of skill-biased technical change
from that of internationalisation. Researchers therefore agree that there is a
substantial need for labour market policies which facilitate the process of
adjustment towards a higher proportion of high-skilled employees.

In
short, from a policy perspective the internationalisation of firms is a major
driver of competitiveness, exerting positive impacts on growth, technological
capabilities, labour productivity and wages and also the aggregate
international performance of an economy.

The
firmʼs decision to invest abroad. Two
findings of the firm-level analysis of internationalisation are especially
relevant. First, self-selection of firms into FDI seems to prevail over
learning effects from internationalisation. Thus, the causality runs from superior
performance to the FDI decision and then (possibly) to some growth effects from
learning, while the observed performance premia are not the result of
internationalisation. Consequently, inducing low-performing to engage in
foreign activities does not turn them into high-performing firms. Second,
aggregate performance (growth, competitiveness) is to a large extent driven by
reallocation effects between well-performing and poorly performing firms. That
is, aggregate competitiveness (productivity) increases because of an increase
in the number of high-performing firms and not so much because of an increase
in the productivity growth of these firms.

Both
the evidence of self-selection of high-performing firms into FDI and the
importance of reallocation effects for aggregate performance lead to the
conclusion that the best policy measures to promote outward FDI are not
subsidies and targeted support, but the promotion of a competitive business
environment in general (Greenaway, 2004). This would ensure an intra-industry
reallocation of resources from the worst-performing to the best-performing
firms with the effect of increasing the MNE base of countries and increasing
aggregate productivity, growth and wages. The policy question, thus, is not so
much which firms to support, but what policy environment ensures reallocations
and leads more firms to reach the threshold levels of performance indicators to
self-select into internationalization.

It is
also crucial to provide conditions which allow small firms and small MNEs to
grow. The analysis has shown a strong relationship between firm size and
multinational activity, both in terms of starting foreign operations and in
terms of the number of affiliates. While the findings do not imply that firms
need to be very large - and a lot of medium-sized firms actually undertake both
intra-EU and extra-EU FDI - the firm size must reach critical levels to cover
the fixed and variable costs of global operations. The growth of SMEs seems to
be especially important in efforts to promote multi-country strategies of MNEs
and FDI into dynamic emerging economies. The firm growth literature finds that
US firms enjoy more dynamic growth than European firms and suggests that there
are still sizeable barriers to firm growth in Europe which need to be
identified properly (Scarpetta et al., 2002; Bartelsman et al., 2004;
Bartelsman et al., 2005; and Navaretti et al., 2011).

From a
policy perspective it will be important to ascertain why firms with similar
size and performance characteristics to MNEs fail to self-select into FDI.
Entry costs could vary across firms due to information asymmetries and
uncertainties (Eaton et al., 2008; Todo, 2011). If the choice to not operate
internationally via FDI is due to firmsʼs different abilities to gather
information about foreign markets, there is room for policy to set up an
infrastructure to alleviate these factors of uncertainty. If the failure to
embark on FDI activities or to broaden the country base of FDI activities is
due to management failures within firms, any policy action in terms of
subsidies ʻwill simply be a waste of resourcesʼ (Greenaway, 2004).
Thus, policy should focus on curing market failures (information and knowledge
problems, missing insurance markets, etc.), while any targeted support and
promotion of particular firms with high internationalisation potential will
always run into problems of ex-ante selection.

Determinants
of FDI flows – how to attract FDI. The
empirical evidence shows that factor cost advantages, the introduction of the euro
and EU membership are driving forces behind FDI in the EU-27. Skills also play
a positive role in attracting FDI in supporting the importance of improving
education and training systems to develop higher levels and better quality
skills in the workforce. While the effects of unit labour costs are larger in
the EU-15 than in the EU-12, tax effects are larger and only significant in the
latter group of countries. Only for greenfield FDI do corporate taxes have a
strong impact in both the EU-12 and EU-15 countries.

Furthermore,
changes in employment protection and the cost of starting a business cannot
explain the change in FDI activity over time but are significant at the
cross-sectional level. Moreover, some determinants (e.g. ICT infrastructure,
intellectual property rights and labour market protection) fail to have a
significant impact on FDI activity when other effects are controlled for. All
these determinants are only significant at the cross-sectional level.

Although
the empirical analysis in this study indicates that in the EU-15 countries,
differences in the corporate tax rate have little impact in attracting FDI to a
country, these differences have generated much debate on corporate tax
consolidation (see Bettendorf et al. 2010), tax competition (Genschel and
Schwarz, 2011) and transfer pricing (Gresik, 2001).

Differences
in tax rates can have negative impacts on productivity growth and in other
areas of the European market. Transfer pricing may have negative consequences
when multinational enterprises reduce their overall tax burden by moving
earnings from subsidiaries in high-tax to low-tax countries through the prices
they set on internal transactions (Gresik, 2001). Estimates of the mean
semi-elasticity of FDI with respect to the tax rate provided in this chapter are
higher for the EU-12 than the EU-15, suggesting that some profit shifting
happens between Eastern and Western Europe. In the EU-12 greenfield FDI
accounts for the majority of FDI, which is more sensitive to taxes than
M&As, which account for the bulk of FDI in the EU-15. As a solution all EU
Member States have in place transfer pricing rules following OECD armʼs
length principle. According to this principle transfer pricing for transactions
within multinationals is considered armʼs length, if it is within a range
of market prices for comparable transactions. However, it may not be easy to
identify the correct armʼs length price for a transaction, as comparable
market prices are not available for some transactions and it is difficult to
monitor all transactions.[45]

A
second solution would be to implement some kind of tax harmonisation, either
partially through the tax base, or fully through both the tax rate and the tax
base (Bettendorf et al., 2010). Harmonised tax systems also provide an
attractive solution to the tax competition problem. Tax
competition encourages a steady decline in the corporate tax rate when
countries maintain relatively lower tax rates or offer tax incentives on a
unilateral basis. This trend has the potential to create certain perverse
incentives through greater differentials, especially if the corporate income
tax rate is below the individual income tax rate (European Commission, 2011). However,
the idea of tax harmonisation remains very controversial, mainly because Member
States generally want to retain sovereignty over their tax systems.

Furthermore,
greenfield FDI is much more sensitive to changes in host- and home country GDP
than total FDI. Since distance may be related to transport costs, improving
transportation infrastructure can help to increase greenfield FDI.

Finally,
a sizable share of the slow growth of FDI stocks in some EU-15 countries can be
attributed to rising unit labour costs. Hence, Member States should attempt to
improve their cost competitiveness by ensuring that rates of real wage growth
do not exceed the rate of labour productivity growth.

Policies
to maximise the benefits of inward FDI. Multinational enterprises can be an important conduit of
international technology transfer and spillovers. Linkages are relevant and the
effects are sizable. Hence, fears that FDI may create an ʻeconomic enclaveʼ
or ʻcathedrals in the desertʼ are not justified. The size of
spillovers and technology transfers is clearly shown to depend on firm-specific
characteristics of local enterprises, especially their absorptive capacity.

Both
technology transfer and knowledge spillovers are strongly dependent on how much
multinationals are embedded in the host country, or the extent to which
multinationals include local enterprises in their global production and
innovation networks. Estimates based on CIS data suggest that local suppliers
to multinational enterprises introduce new products more often than
non-collaborators. This indicates that technology transferred to local firms
may also lead to spillovers often associated with competitive behaviour. An
implication of these findings is that neither inward FDI nor spillovers should
be targeted as policy variables, but instead industrial policy should focus on
encouraging the formation of networks between local enterprises and multinational
enterprises (see more about this in Chapter 5). Targeted incentives to
promote the strengthening of linkages can be important but the use of such
incentives should be compatible with the EU regulations on subsidies and
countervailing measures.

Estimates
based on firm-level data for the EU-12 suggest that labour productivity growth
in local firms is significantly positively correlated with the extent of
backward linkages from foreign-owned industries to local firms, but not with
the presence of foreign-owned firms in the same industry. Estimates based on
CIS data for the EU-12 also show that local firms with backward linkages from
multinational enterprises have a significantly higher average employment growth
rate (except for small firms). Furthermore, the magnitude of the employment
effect through backward linkages increases with the absorptive capacity of
local firms. These estimates confirm the need to introduce policies that
facilitate the transfer of technology between local firms and multinationals
and assist firms in building capabilities.

Investment
promotion in practice. There is considerable
controversy over what kind of investment promotion measures the EU and/or
individual Member States should adopt. Many national and regional investment promotion
agencies offer services to reduce transaction cost and information asymmetries
for foreign firms. These can ease the burden of bureaucratic procedures and
help to better assess the costs and opportunities in a particular business
environment. Harding and Javorcik (2011) suggest that investment promotion does
not work in countries where information asymmetries are relatively low and
bureaucratic procedures less complex, but that it could work in less developed
countries, including the EU-12 countries. The above statistical analysis
reveals, however, that information asymmetries and other regulations did not
discourage investors in the EU-12. Furthermore, the trend toward consistency of
external relations and the internal market will likely further reduce these
barriers over the next few years. In any case, policy can benefit from the
mutual learning about good practices among the variety of approaches and
agencies currently operating in the different Member States.

Free
movement of capital is one of the four freedoms of the internal market which
means that there should not be any barriers to or restrictions on capital
movements within the European Union. While this policy is resolutely part of EU
law, harmonisation of corporate taxation remains highly controversial.

Expanding
the common commercial policy. The
common commercial policy, enshrined in the Treaty of Rome in 1957, is central
to the European Unionʼs  external relations. Article
206 of the Treaty on the Functioning of the European Union (Lisbon
Treaty), which entered into force in 2009, requires external relations to be harmonised
by progressive abolishing of restrictions on international trade and FDI, and the
lowering customs and other barriers. The Lisbon Treaty expands the scope of the
common commercial policy by providing the EU with exclusive competence to
negotiate international agreements concerning FDI.

The EU
pays particular attention to develop a common international investment policy:
the Communication ʻTowards a comprehensive European international
investment policyʼ COM(2010) 343 explores how the EU may develop an
international investment policy that increases the EUʼs competitiveness
and thus contribute to smart, sustainable and inclusive growth, as set out in
the Europe 2020 Strategy.[46]
In July 2010, the European Commission released another communication on
establishing transitional arrangements for bilateral investment agreements
between Member States and third countries (COM(2010)344). By improving investment
protection and reducing the investorʼs risk of entering a foreign market
these agreements reduce the costs of investments. Furthermore, from the host
country perspective clear and enforceable rules add to their attractiveness as
a destination for FDI.

On the one
hand, the EU should ensure ʻan open, properly and fairly regulated
business environmentʼ for investors throughout Europe. Article 173 of
the Treaty on the Functioning of the European Union specifies a number of
objectives to ensure all necessary conditions for the competitiveness of the EU
industry. As such FDI can play an important role in delivering these
objectives, such as ʻspeeding up the adjustment of industry to structural
changes and better exploitation of industrial potential of policies of
innovation, research and technological developmentʼ. At the same time Article
173 highlights the importance of a favourable business environment, a
crucial factor for attracting foreign investors. More recently, on 3 July 2012, the European Parliament adopted a non-legislative resolution on Attractiveness
of investing in Europe (2011/2288(INI). The basic approach of the resolution is that Europe needs more
investment from both EU and non-EU investors. It covers a range of
recommendations, such as exploiting the EUʼs position, maximising cohesion
policy, improving access to finance and education, combating tax evasion in
order to provide better framework conditions for attracting FDI.

On
the other hand the Communication COM(2010) 343  points
out that ʻthe EU should ensure that EU investors abroad enjoy a level
playing fieldʼ. The Communication on ʻAn Integrated Industrial
Policy for the Globalisation Eraʼ[47]
among others highlights the role of internationalisation of enterprises
(especially that of SMEsʼ) both within and outside the EU and the
enterprises ability to ʻaccess international markets and exploit global
value chainsʼ.

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              Appendix

Table A.1: Panel data estimates of the determinants of
bilateral FDI stocks in the EU-27 countries

|| Fixed effects estimates ||  HT-estimates ||  HT-estimates || HT-estimates

|| coef || || t || t clust adj. a) || coef || || T || coef || || t || coef || ||  T

 host ln GDP in EUR host country, t-1 || 0.83 || \*\*\* || 6.67 || 2.77 || 1.00 || \*\*\* || 15.10 || 1.01 || \*\*\* || 15.19 || 1.05 || \*\*\* || 13.69

 parent ln GDP in EUR parent country, t-1 || 0.85 || \*\*\* || 11.03 || 5.35 || 0.81 || \*\*\* || 11.35 || 0.80 || \*\*\* || 11.08 || 0.80 || \*\*\* || 11.23

 host effective average corporate tax rate t-1 || -1.80 || \*\*\* || -4.42 || -1.61 || -1.56 || \*\*\* || -4.03 || || || || -1.52 || \*\*\* || -3.96

 host statutory corporate tax rate, t-1 || || || || || || || || -0.64 || \* || -1.85 || || ||

 parent statutory corporate tax rate, t-1 || || || || || || || || -0.41 || \*\* || -0.94 || || ||

 host unit labour costs, t-1 || -0.83 || \*\*\* || -2.74 || -1.55 || -1.02 || \*\*\* || -3.73 || -1.05 || \*\*\* || -3.80 || -0.91 || \*\*\* || -3.30

 parent ln tertiary graduates share, t-1 || 0.56 || \*\*\* || 3.81 || 2.49 || 0.59 || \*\*\* || 4.12 || 0.55 || \*\*\* || 3.79 || 0.65 || \*\*\* || 4.56

 parent ln R&D/GDP ratio, t-1 || 0.50 || \*\*\* || 4.22 || 1.93 || 0.49 || \*\*\* || 4.26 || 0.50 || \*\*\* || 4.31 || 0.45 || \*\*\* || 3.91

 ln distance || || || || || -1.64 || \*\*\* || -18.93 || -1.63 || \*\*\* || -19.0 || -1.65 || \*\*\* || -19.4

 common language || || || || || 0.85 || \*\* || 2.51 || 0.83 || \*\* || 2.49 || 0.78 || \*\* || 2.31

 former colony || || || || || 1.25 || \*\*\* || 3.27 || 1.27 || \*\*\* || 3.34 || 1.28 || \*\*\* || 3.39

 contiguity || || || || || -0.88 || \*\*\* || -2.68 || -0.90 || \*\*\* || -2.77 || -0.93 || \*\*\* || -2.88

 year 2001 (base year 2000) || -0.17 || \*\*\* || -3.07 || -2.96 || -0.17 || \*\*\* || -3.22 || -0.15 || \*\*\* || -2.81 || -0.17 || \*\*\* || -3.19

 year 2002 || -0.11 || \*\* || -2.01 || -1.45 || -0.13 || \*\* || -2.38 || -0.11 || \*\* || -2.08 || -0.13 || \*\* || -2.47

 year 2003 || -0.06 || || -0.97 || -0.68 || -0.07 || || -1.37 || -0.06 || || -1.10 || -0.08 || || -1.58

year 2004 || 0.07 || || 1.08 || 0.75 || 0.05 || || 0.91 || 0.06 || || 1.03 || 0.05 || || 0.97

year 2005 || 0.06 || || 0.93 || 0.65 || 0.04 || || 0.69 || 0.06 || || 0.92 || 0.06 || || 1.08

year 2006 || 0.08 || || 1.15 || 0.78 || 0.06 || || 0.93 || 0.09 || || 1.31 || 0.07 || || 1.08

year 2007 || 0.10 || || 1.26 || 0.67 || 0.07 || || 0.96 || 0.10 || || 1.33 || 0.07 || || 1.08

year 2008 || 0.00 || || 0.03 || 0.02 || -0.03 || || -0.41 || 0.00 || || 0.06 || -0.01 || || -0.18

year 2009 || 0.00 || || 0.04 || 0.02 || -0.03 || || -0.33 || 0.01 || || 0.16 || -0.04 || || -0.43

year 2010 || 0.12 || || 1.33 || 0.69 || 0.10 || || 1.21 || 0.13 || || 1.43 || 0.11 || || 1.25

year 2004\*EU-12 || 0.08 || || 0.95 || 1.14 || 0.07 || || 0.87 || 0.10 || || 1.23 || || ||

year 2005\*EU-12 || 0.17 || \*\* || 2.09 || 1.97 || 0.16 || \*\* || 2.00 || 0.19 || \*\* || 2.47 || || ||

year 2006\*EU-12 || 0.14 || \* || 1.79 || 1.69 || 0.12 || || 1.58 || 0.15 || \* || 1.94 || || ||

year 2007\*EU-12 || 0.27 || \*\*\* || 3.50 || 2.51 || 0.24 || \*\*\* || 3.35 || 0.27 || \*\*\* || 3.75 || || ||

year 2008\*EU-12 || 0.32 || \*\*\* || 4.11 || 2.68 || 0.29 || \*\*\* || 3.91 || 0.31 || \*\*\* || 4.23 || || ||

year 2009\*EU-12 || 0.25 || \*\*\* || 2.93 || 1.79 || 0.20 || \*\*\* || 2.58 || 0.22 || \*\*\* || 2.77 || || ||

year 2010\*EU-12 || 0.39 || \*\*\* || 3.96 || 2.23 || 0.35 || \*\*\* || 3.77 || 0.38 || \*\*\* || 4.06 || || ||

year 2007\*(dBG | dRO) || || || || || || || || || || || 0.65 || \*\*\* || 4.59

year 2008\*(dBG | dRO) || || || || || || || || || || || 0.63 || \*\*\* || 4.43

year 2009\*(dBG | dRO) || || || || || || || || || || || 0.47 || \*\* || 2.35

year 2010\*(dBG | dRO) || || || || || || || || || || || 0.75 || \*\*\* || 3.72

year 2007\*newEURO || || || || || || || || || || || 0.19 || || 0.83

year 2008\*newEURO || || || || || || || || || || || -0.04 || || -0.23

year 2009\*newEURO || || || || || || || || || || || 0.19 || || 1.38

year 2010\*newEURO || || || || || || || || || || || 0.31 || \* || 1.93

constant || -34.5 || \*\*\* || -9.26 || -3.91 || -25.3 || \*\*\* || -11.2 || -25.5 || \*\*\* || -11.2 || -26.1 || \*\*\* || -10.9

host country effects (p-value) || 0.00 || || || || 0.00 || || || 0.00 || || || 0.00 || ||

home country effects (p-value) || 0.00 || || || || 0.00 || || || 0.00 || || || 0.00 || ||

R2 within || 0.34 || || || || 0.68 || || || 0.67 || || || 0.67 || ||

number of observations || 5116 || || || || 5116 || || || 5116 || || || 5116 || ||

number of country-pairs || 626 || || || || 626 || || || 626 || || || 626 || ||

Note: The
dependent variable is the log of bilateral inward FDI stock held by EU country
i from country j; a)t-values are based on cluster-adjusted standard errors
accounting for common host country effects. \*\*\*, \*\* and \*
denote statistical significance at 1 percent, 5 percent and
10 percent levels, respectively. The within transformation is used to wipe
out country-pair fixed effects. In the HT-estimator all time varying variables
except time dummies and their interaction terms are assumed to be endogenous.
The sample includes 26 home countries: Australia, Austria, Belgium, Brazil,
Canada, China, Denmark, Finland, France, Germany, Hong Kong, India, Ireland,
Italy, Japan, Luxembourg, the Netherlands, Norway, Portugal, Russia, South
Korea, Spain, Sweden, Switzerland, the United Kingdom and the United States.
The host countries are the EU-27 countries.

Source: European Commission, World Bank, OECD, Eurostat Eurobase.

The empirical specification is based on a standard gravity
equation augmented by several host and home country factors:

where i is the home country and j is the host country and Ln
is the natural logarithm. The variables are defined as follows:

 is
the inward FDI stock (book value of foreign assets) in current million EURO
held by a EU country j from parent country i in a given year (or alternatively  plus
EUR 1); in addition Greenfield FDI flows from country i to country j is used;

,
 are
home and host country GDP in current EUR;

 is
the distance between capital cities of the investing and host country;

,
 are
the effective average tax rate for the nonfinancial sector of the home and host
country respectively;

,
 are
unit labour costs of the home and host country respectively;

,
,
are the share of labour force aged 15 to 74 with tertiary education (levels 5
and 6) of the home and host country respectively;

 is
the absolute value of the difference in GDP per capita in purchasing power
parities between the source and the host country respectively;

is
a time-varying dummy variable which takes the value of one if the parent
country belongs to the Euro area, ,
and the host country introduced the EURO,  (Slovenia
in 2007, Cyprus and Malta 2008 and Slovakia starting from 2009) and zero
otherwise respectively;

 takes
the value one if the parent country is a EU member state,  and
the host country is joining the EU,  (2004
for EU-10 countries and 2007 for Bulgaria and Romania) respectively;

  represents
a set of time varying host and parent country factor variables (i.e.,          R&D/GDP
ratio, FDI regulatory restrictiveness index, strength of legal rights index for
getting credits, strength of investor protection index, cost of starting a
business as a percentage of income per capita, employment protection
legislation; top marginal tax rate, protection of intellectual property, hiring
and firing practices, labor force share with wages set by centralized collective
bargaining, fixed broadband internet subscribers, internet users per 100
people, total tax rate of businesses in percent of commercial profits);

 represents
time invariant control variables (i.e. contiguity, sharing the same language
and when they share a (former) colonial link);

t are time dummies (TD);  are
time effects;  are
country-pair specific effects and  is
the error term.

The gravity equation contains bilateral country-pair fixed effects,  to
control for unobserved time-invariant heterogeneity includes common time
effects, .
In addition, a large number of policy factors of the home and host country are
included.

Table A.2.: Means and correlations
coefficients between the ratio of the FDI stock to (home and host country) GDP
and the explanatory variables

|| means || means || correlation with the ratio of inward FDI stock to host country GDP

|| unweighted

host country factors: || 2000 || 2010 || coef. || p-value || # of observations

 adjusted top statutory tax rate on corporate income  in % || 31.9 || 23.3 || -0.01 || 0.46 || 6228

 effective average corporate tax rate in % || 27.5 || 21.8 || -0.02 || 0.12 || 6228

 bilateral effective average corporate tax rate (host) in % || 31.3 || 25.2 || -0.13 || 0.00 || 3238

 total tax rate (% of commercial profits) || 50.3 || 45.4 || -0.10 || 0.00 || 2909

 top marginal tax rate in % || 55.4 || 50.3 || -0.04 || 0.00 || 5648

 unit labour costs (ratio) || 0.54 || 0.72 || -0.01 || 0.33 || 5845

 hourly wage compensation in EUR || 13.8 || 18.8 || 0.08 || 0.00 || 6204

 tertiary graduates share in % || 16.5 || 22.0 || 0.08 || 0.00 || 6228

 R&D/GDP ratio in % || 1.2 || 1.6 || 0.02 || 0.07 || 6083

 fixed broadband internet subscribers (per 100 people) || 0.8 || 24.2 || 0.10 || 0.00 || 5947

 internet users per 100 people || 19.6 || 69.7 || 0.10 || 0.00 || 6228

 strength of investor protection index (0-10) (10=highest investor protection) || 5.5 || 5.6 || 0.04 || 0.03 || 2909

 protection of intellectual property (0-10) (10=highest protection) || 6.6 || 6.9 || 0.09 || 0.00 || 5624

 getting credit - strength of legal rights index (0-10) (10=best) || 6.7 || 7.0 || 0.05 || 0.00 || 4032

 FDI regulatory restrictiveness index (0-1) (0=open; 1=closed) || 0.07 || 0.05 || -0.09 || 0.00 || 5516

 cost of starting a Business (% of income per capita) || 11.4 || 5.6 || -0.06 || 0.00 || 4564

 hiring and firing practices (1-10) (1=least regulated, 10=most regulated) || 3.6 || 4.1 || 0.04 || 0.01 || 5604

 employment protection legislation, (0-6) (0= least and 6 most restrictive || 2.13 || 2.09 || -0.07 || 0.00 || 3477

 labour force share with wages set by centralized collective bargaining (1-10) (=1 highly centralized, 10=least centralized, i.e. best) || 5.7 || 5.7 || 0.00 || 0.79 || 5604

 GDP per capita in int. $ US ppp || 23025 || 26711 || 0.20 || 0.00 || 6228

 distance in kilometres || 3969.3 || || -0.23 || 0.00 || 0.00

 former colony || 7.0 || || 0.20 || 0.00 || 0.00

 common language || 7.1 || || 0.26 || 0.00 || 0.04

 contiguity || 3.6 || || 0.27 || 0.00 || 0.00

|| || || correlation with the ratio of outward FDI stock to home country GDP

home country factors: || 2000 || 2010 || correlation || p-value || # of observations

 adjusted top statutory tax rate on corporate income  in % || 34.3 || 28.2 || -0.01 || 0.28 || 6237

 effective average corporate tax rate in % || n.a. || n.a. || || ||

 bilateral effective average corporate tax rate (host) in % || 31.3 || 25.2 || -0.01 || 0.48 || 3238

 total tax rate (% of commercial profits) || 51.1 || 46.5 || -0.19 || 0.00 || 3081

 top marginal tax rate in % || 52.5 || 49.1 || -0.03 || 0.02 || 5511

 unit labour costs (ratio) || 0.59 || 0.72 || -0.11 || 0.00 || 4864

 hourly wage compensation in EUR || 19.2 || 24.4 || 0.09 || 0.00 || 6206

 tertiary graduates share in % || 20.6 || 26.6 || -0.01 || 0.35 || 6237

 R&D/GDP ratio in % || 1.8 || 2.4 || -0.03 || 0.01 || 5974

 fixed broadband internet subscribers (per 100 people) || 1.7 || 26.2 || 0.05 || 0.00 || 6137

 internet users per 100 people || 27.6 || 71.3 || 0.08 || 0.00 || 6172

 strength of investor protection index (0-10) (10=highest investor protection) || 5.9 || 6.0 || 0.04 || 0.05 || 2907

 protection of intellectual property (0-10) (10=highest protection) || 7.2 || 7.5 || 0.06 || 0.00 || 5676

 getting credit - strength of legal rights index (0-10) (10=best) || 6.6 || 6.9 || 0.00 || 0.79 || 4268

 FDI regulatory restrictiveness index (0-1) (0=open; 1=closed) || 0.15 || 0.10 || -0.12 || 0.00 || 6237

 cost of starting a Business (% of income per capita) || 9.9 || 6.5 || -0.03 || 0.07 || 4809

 hiring and firing practices (1-10) (1=least regulated, 10=most regulated) || 3.8 || 4.5 || -0.04 || 0.00 || 5676

 employment protection legislation, (0-6) (0= least and 6 most restrictive || 1.88 || 1.96 || 0.07 || 0.00 || 4068

 labour force share with wages set by centralized collective bargaining (1-10) (=1 highly centralized, 10=least centralized, i.e. best) || 5.4 || 5.7 || -0.04 || 0.00 || 5676

 GDP per capita in int. $ US ppp || 27638 || 31103 || 0.29 || 0.00 || 6237

 distance in kilometres || || || || ||

 former colony || || || || ||

 common language || || || || ||

 contiguity || || || || ||

Note: Data refer to unweighted means for the year 2000
and 2010 or the latest available year. In some cases data refer to 2003 and
2004.

Source: European Commission, World Bank, OECD, Eurostat Eurobase.

Table A.3: Pseudo Poisson maximum
likelihood (PPML) estimates of the determinants of bilateral greenfield FDI
flows in the EU-27 countries (marginal effects)

|| Host-countries: EU-27, home-countries: 26 OECD                                                             and BRICs

|| (i) || (ii) || (iii)

||  marg eff || || t ||  marg eff || || t ||  marg eff || || t

host ln GDP in EUR host country, t-1 || 5.53 || \*\*\* || 3.21 || 3.36 || || 1.25 || 5.11 || \*\* || 2.03

parent ln GDP in EUR parent country, t-1 || 2.96 || \*\*\* || 3.06 || 3.17 || \*\*\* || 3.17 || 3.13 || \*\*\* || 3.14

host effective average corporate tax rate, t-1 || -11.98 || \*\*\* || -2.93 || -10.90 || \*\*\* || -2.58 || -12.70 || \*\*\* || -3.16

host ln hourly wages costs, t-1 || -6.05 || \*\*\* || -2.76 || -6.17 || \*\*\* || -2.58 || -7.18 || \*\*\* || -2.99

host ln share of tertiary education, t-1 || 2.32 || || 1.53 || || || || || ||

parent ln share of tertiary education, t-1 || 2.68 || \* || 1.87 || || || || || ||

parent ln R&D/GDP ratio, t-1 || 3.98 || \*\*\* || 3.44 || || || || || ||

GDP per capita dissimilarity, t-1 || 3.90 || \*\*\* || 4.66 || || || || || ||

new EMU members 2007, 2008, 2009 || || || || || || || 1.76 || \*\* || 2.31

new EU members 2007 || || || || 2.07 || \*\*\* || 3.92 || || ||

ln distance || -2.07 || \*\*\* || -3.84 || -1.84 || \*\*\* || -3.14 || -1.79 || \*\*\* || -3.01

Contiguity || -0.66 || || -0.93 || -0.60 || || -0.79 || -0.60 || || -0.79

common language || 1.23 || || 1.77 || 1.01 || || 1.44 || 1.05 || || 1.50

former colony || 1.19 || || 1.26 || 1.22 || || 1.26 || 1.22 || || 1.27

time dummy variables || yes || || || yes || || || yes || ||

host country effects || yes || || || yes || || || yes || ||

home country effects || yes || || || yes || || || yes || ||

R2 || 0.44 || || || 0.426 || || || 0.42 || ||

number of observations || 5348 || || || 5348 || || || 5348 || ||

number of country-pairs || 688 || || || 688 || || || 688 || ||

Note: The
dependent variable is the log of bilateral greenfield FDI flows from country i
to country j in current euros. t-values are based on cluster-adjusted standard
errors accounting for common host country effects. \*\*\*, \*\*
and \* denote statistical significance at 1 percent,
5 percent and 10 percent levels, respectively. The marginal effects
can be interpreted as elasticities and semi-elasticities.

Source: European Commission, World Bank, OECD, Eurostat Eurobase, fDi
Intelligence database.

Table A. 4: ZINB estimates of the number of subsidiaries
and market coverage of EU-15 multinational firms

|| Manufacturing || Non-Manufacturing

|| Number of subsidiaries || Market coverage || Number of subsidiaries || Market coverage

|| Coef. || || z- value || Coef. || || z- value || Coef. || || z- value || Coef. || || z- value

|| (1) || (2) || (3) || (4)

|| Logit model component explaining zero subsidiaries

log age in years || -0.39 || \*\*\* || -5.6 || -0.39 || \*\*\* || -5.2 || -0.04 || || -0.8 || 0.00 || || -0.1

log number of shareholders || 0.31 || \*\*\* || 6.4 || 0.34 || \*\*\* || 6.5 || 0.19 || \*\*\* || 5.8 || 0.21 || \*\*\* || 5.6

log employment || -1.33 || \*\*\* || -28.8 || -1.37 || \*\*\* || -28.1 || -0.97 || \*\*\* || -27.7 || -1.05 || \*\*\* || -25.6

log turnover per employee || -0.28 || \*\*\* || -4.3 || -0.30 || \*\*\* || -4.2 || -0.09 || \*\*\* || -2.7 || -0.09 || \*\* || -2.4

log total fixed assets per employee || -0.80 || \*\*\* || -12.8 || -0.86 || \*\*\* || -12.9 || -0.74 || \*\*\* || -25.0 || -0.80 || \*\*\* || -24.1

log intangible assets to fixed assets || -0.07 || \*\*\* || -3.1 || -0.07 || \*\*\* || -2.8 || -0.06 || \*\*\* || -4.0 || -0.03 || \*\* || -2.0

Industry dummy || yes || || || yes || || || yes || || || yes || ||

Constant || 12.40 || \*\*\* || 27.5 || 12.86 || \*\*\* || 26.8 || 9.26 || \*\*\* || 34.8 || 9.49 || \*\*\* || 31.6

lnalpha || 1.08 || \*\*\* || 32.8 || 0.88 || \*\*\* || 26.2 || 1.63 || \*\*\* || 46.0 || 1.42 || \*\*\* || 36.5

alpha || 2.93 || || || 2.42 || || || 5.08 || || || 4.15 || ||

|| Marginal effects of the count data component of the model

log age in years || 0.022 || \*\*\* || 13.2 || 0.020 || \*\*\* || 12.1 || 0.004 || \*\*\* || 7.0 || 0.003 || \*\*\* || 5.3

log number of shareholders || -0.005 || \*\*\* || -3.6 || -0.006 || \*\*\* || -4.3 || 0.002 || \*\*\* || 2.9 || 0.000 || || 0.1

log employment || 0.071 || \*\*\* || 37.1 || 0.066 || \*\*\* || 36.7 || 0.030 || \*\*\* || 43.1 || 0.027 || \*\*\* || 39.5

log turnover per employee || -0.003 || || -1.8 || -0.001 || || -0.5 || 0.0002 || || 0.4 || 0.001 || || 1.3

log total fixed assets per employee || 0.062 || \*\*\* || 30.6 || 0.056 || \*\*\* || 29.3 || 0.028 || \*\*\* || 43.4 || 0.024 || \*\*\* || 43.6

log intangible assets to fixed assets || 0.003 || \*\*\* || 5.8 || 0.003 || \*\*\* || 5.5 || 0.000 || \*\*\* || 3.3 || 0.001 || \*\*\* || 3.4

Industry dummy || yes || || || yes || || || yes || || || yes || ||

number of observations || 88,690 || || || 88,690 || || || 248,783 || || || 248,783 || ||

number of nonzero observations || 7,321 || || || 7,321 || || || 10,481 || || || 10,481 || ||

Note:
\*\*\*, \*\*, \* indicates significance at the 1-, 5- and 10-percent-level,
respectively. Model specification is not shown.

Source: AMADEUS database
(2011 release), WIFO calculations.

Table A.5 - Estimates of the
Barro-type growth model (pooled OLS)

|| Total sample || EU-15+NO and CH || EU-12 + TR

|| Impact of FDI inflows as a percentage of GDP

|| coef || || t || coef || || t || coef || || t

log GDP per capita, PPP (const. 2005 intern. $) lagged one period || -0.004 || || -0.77 || -0.021 || \*\*\* || -2.73 || -0.01 || || -0.87

Investment % GDP || 0.203 || \*\*\* || 2.57 || 0.08 || \* || 1.93 || 0.333 || \*\* || 2.36

Average years of schooling || 0.001 || || 1.05 || 0.002 || \* || 1.77 || 0 || || 0.04

Foreign direct investment inflows % GDP || 0.104 || \*\*\* || 2.69 || 0.106 || \*\* || 2.34 || 0.203 || \* || 1.9

Constant || 0.001 || || 0.02 || 0.194 || \*\*\* || 2.81 || 0.035 || || 0.33

R2 || 0.166 || || || 0.232 || || || 0.227 || ||

number of observations || 128 || || || 82 || || || 46 || ||

number of countries || 29 || || || 17 || || || 12 || ||

|| Impact of FDI inflows as a percentage of GDP adjusted for double counting

|| coef || || t || coef || || t || coef || || t

log GDP per capita, PPP (const. 2005 intern. $) lagged one period || -0.004 || || -0.77 || 0.008 || || 0.01 || -0.01 || || -0.87

investment % GDP adjusted by FDI inflows || 0.203 || \*\* || 2.57 || 0.08 || \* || 1.93 || 0.333 || \*\* || 2.36

average years of schooling || 0.001 || || 1.05 || 0.002 || \* || 1.77 || 0 || || 0.04

foreign direct investment inflows % GDP || 0.307 || \*\*\* || 3.68 || 0.186 || \*\*\* || 2.65 || 0.536 || \*\*\* || 3.75

Constant || 0.001 || || 0.02 || 0.194 || \*\*\* || 2.81 || 0.035 || || 0.33

R2 || 0.166 || || || 0.232 || || || 0.226 || ||

number of observations || 128 || || || 82 || || || 46 || ||

number of countries || 29 || || || 17 || || || 12 || ||

|| Impact of FDI inward stock GDP ratio

|| coef || || t || coef || || t || coef || || t

log GDP per capita, PPP (const. 2005 intern. $) lagged one period || -0.006 || \* || -1.47 || -0.018 || \*\*\* || -2.37 || -0.026 || \* || -1.95

Investment % GDP || 0.215 || \*\* || 2.92 || 0.076 || || 1.82 || 0.336 || \*\*\* || 3.11

Average years of schooling || 0 || || 0.05 || 0.001 || || 1.16 || -0.002 || || -1.06

Foreign direct investment stock % GDP || 0.024 || \*\* || 3.91 || 0.013 || \*\*\* || 2.21 || 0.08 || \*\* || 3.43

Constant || 0.031 || || 0.62 || 0.171 || \*\* || 2.44 || 0.191 || || 1.57

R2 || 0.227 || || || 0.225 || || || 0.421 || ||

number of observations || 129 || || || 82 || || || 47 || ||

number of countries || 29 || || || 17 || || || 12 || ||

Note: Dependent
variable is real GDP per capita growth. \*\*\*, \*\* and \*
denote significance at the 1 percent, 5 percent and 10 percent
level. t-values are based on robust standard errors. The sample for EU-12 +
Turkey includes the following countries and years: MT and TR all for the five
year periods 1985-1990, 1990-1995, 1995-2000, 2000-2005 and 2005-2010; , BG,
EE, HU. LV, RO and SK all for the five year periods 1990-1995, 1995-2000,
2000-2005 and 2005-2010; CZ, PL, LT and SI all for the five-year periods
1995-2000, 2000-2005 and 2005-2010. The sample for EU-15 + NO and CH includes
following countries and years: AT, BE, CH, DE, DK, ES, FI, FR, EL, IE, IT, NL,
NO, PT, SE and UK all for the five year periods 1985-1990, 1990-1995, 1995-2000,
2000-2005 and 2005-2010; and LU for the five-year periods 2000-2005 and
2005-2010.

Source: World Development Indicators database, Barro-Lee database, UNCTAD.

Table A.6 - Productivity effects of
foreign presence in the same industry and in customer industries (backward
production linkages)

(Manufacturing, EU-15 countries) || Robust regression method

|| (i) || || || (ii) || || || (iii) || ||

|| coef || || t || coef || || t || coef || || t

Initial employment share of foreign affiliates || 0.10 || \*\*\* || 4.01 || 0.11 || \*\*\* || 4.14 || 0.09 || \*\*\* || 3.56

Initial employment share of foreign affiliates among customers (FORCUST) || 0.11 || \*\*\* || 2.77 || 0.08 || \* || 1.77 || -0.01 || || -0.25

Relative labour productivity domestic/foreign sector || 0.01 || || 1.32 || 0.01 || || 0.95 || -0.02 || || -1.47

Av. annual labour productivity growth foreign sector || || || || 0.28 || \*\*\* || 4.70 || 0.33 || \*\*\* || 5.80

Interaction term rel. labour productivity X FORCUST || || || || || || || 0.20 || \*\* || 2.28

Industry and country dummies || yes || || || yes || || || yes || ||

Constant || 0.00 || 0.01 || -0.09 || -0.02 || || -2.08 || 0.02 || || 1.07

number of observations || 94 || || || 94 || || || 94 || ||

number of co || 11 || || || 11 || || || 11 || ||

number of industries || 11 || || || 11 || || || 11 || ||

Interaction term (p-valued || || || || || || || 0.025 || ||

Impact of initial foreign employment share among customers with varying levels of the relative labour productivity

Relative labour productivity domestic/foreign sector: || || || || || || || || ||

0.50 || || || || || || || 0.09 || ||

0.60 || || || || || || || 0.11 || ||

0.70 || || || || || || || 0.13 || ||

0.80 || || || || || || || 0.15 || ||

0.90 || || || || || || || 0.17 || ||

1.00 || || || || || || || 0.19 || ||

(Manufacturing EU-12 countries) || Robust regression method ||

|| (i) || || || (ii) || ||

|| coef || || t || coef || || t

Initial employment share of foreign affiliates || 0.48 || \*\*\* || 2.85 || 0.57 || \*\* || 3.57

Initial employment share of foreign affiliates among customers || 0.88 || \*\* || 2.30 || 0.04 || || 0.05

Relative labour productivity domestic/foreign sector || -0.06 || || -1.18 || -0.24 || || -1.30

Av. annual labour productivity growth foreign sector || || || || || ||

Interaction term || || || || 1.25 || || 1.14

Industry and country dummies || yes || || || yes || ||

Constant || -0.12 || || -1.11 || -0.04 || || -0.31

number of observations || 45 || || || 45 || ||

number of co || 6 || || || 6 || ||

number of industries || 11 || || || 11 || ||

Interaction term (p-value) || || || || 0.10 || ||

Impact of initial employment share of foreign affiliates among customers with varying levels of the relative labour productivity level

Relative labour productivity domestic/foreign sector: || || || || coef. || ||

0.50 || || || || 0.66 || ||

0.60 || || || || 0.79 || ||

0.70 || || || || 0.91 || ||

0.80 || || || || 1.04 || ||

0.90 || || || || 1.16 || ||

1.00 || || || || 1.29 || ||

Note: \*\*\*,
\*\* and \* denote significance at the 1 percent,
5 percent and 10 percent level. Sector and country dummy variables are
included but not reported. t-values of the OLS estimates are based on
heteroskedasticity consistent standard errors. FORCUST measures the backward
linkage from foreign owned firms to domestically owned firms. This table is
based on yet unpublished results from the EU funded project INNO Grips ENTR-09-11-LOT2.

Source: Inward FATS and
National Accounts, Eurostat.

Table A.7 - Productivity effects of
foreign presence in the same and customer industries at the firm level (EU-12
countries)

|| Total sample || Firms with 25 and more employee || Firms with 24 and less employees

|| coef || || t || coef || || t || coef || || T

foreign employment share in the same industry, '03 || -0.76 || \*\*\* || -2.82 || -0.55 || \*\* || -2.32 || -1.01 || \*\*\* || -3.68

foreign employment share in the customer industries, '03 || 0.83 || \*\*\* || 2.85 || 0.62 || \*\* || 2.54 || 1.13 || \*\*\* || 3.49

relative productivity level, 2003 || -0.13 || \*\*\* || -4.77 || -0.11 || \*\*\* || -5.37 || -0.14 || \*\*\* || -3.98

growth rate of fixed assets in const. Prices || 0.06 || \*\*\* || 9.81 || 0.10 || \*\*\* || 7.46 || 0.03 || \*\*\* || 3.39

country and industry dummies || yes || || || yes || || || yes || ||

Constant || -0.02 || . || . || 0.26 || \*\*\* || 2.50 || 0.66 || \*\*\* || 4.77

R2 || 0.31 || || || 0.25 || || || 0.33 || ||

number of observations || 32959 || || || 18035 || || || 14924 || ||

|| Newly founded firms (2001 & older) || Mature firms (2000 & younger) || || ||

|| coef || || t || coef || || t || || ||

foreign employment share in the same industry, '03 || -0.50 || \*\* || -2.22 || -0.88 || \* || -1.80 || || ||

foreign employment share in the customer industries, '03 || 0.26 || || 1.41 || 4.90 || \*\*\* || 4.29 || || ||

relative productivity level, 2003 || -0.08 || \*\*\* || -6.33 || -0.16 || \*\*\* || -3.84 || || ||

growth rate of fixed assets in const. Prices || 0.06 || \*\*\* || 7.74 || 0.06 || \*\*\* || 6.25 || || ||

country and industry dummies || yes || || || yes || || || || ||

Constant || 0.07 || || 1.29 || 0.59 || \*\*\* || 5.27 || || ||

R2 || 0.17 || || || 0.38 || || || || ||

number of observations || 12854 || || || 21303 || || || || ||

|| low productivity growth (Q1) ||  low medium prod. Growth (Q2) || || ||

|| coef || || t || coef || || t || || ||

foreign employment share in the same industry, '03 || 0.03 || || 1.37 || 0.00 ||   || 0.53 || || ||

foreign employment share in the customer industries, '03 || 0.02 || || 0.93 || 0.01 ||   || 1.59 || || ||

relative productivity level, 2003 || -0.01 || \*\*\* || -2.92 || 0.00 ||   || -0.86 || || ||

growth rate of fixed assets in const. Prices || -0.02 || \*\*\* || -5.69 || 0.00 || \*\*\* || 4.07 || || ||

country and industry dummies || yes || || || yes || || || || ||

Constant || -0.13 || \*\*\* || -12.96 || 0.06 || \*\*\* || 14.66 || || ||

R2 || 0.14 || || || .0.03 || || || || ||

number of observations || 8227 || || || 7963 || || || || ||

|| med-high productivity growth (Q3) ||  very high productivity growth (Q4) || || ||

|| coef || || t || coef || || t || || ||

foreign employment share in the same industry, 2003 || -0.03 || || -1.60 || -0.51 || \*\*\* || -3.07 || || ||

foreign employment share in the customer industries,'03 || 0.06 || \*\*\* || 2.81 || 0.70 || \*\*\* || 2.76 || || ||

relative productivity level, 2003 || 0.00 || || -1.15 || -0.22 || \*\*\* || -2.92 || || ||

growth rate of fixed assets in const. Prices || 0.01 || \*\*\* || 3.74 || 0.03 || \*\* || 2.15 || || ||

country and industry dummies || yes || || || yes || || || || ||

Constant || 0.17 || \*\*\* || 20.22 || 0.66 || \*\*\* || 4.63 || || ||

R2 || 0.05 || || || 0.13 || || || || ||

number of observations || 8474 || || || 8295 || || || || ||

Note: The dependent variable is average annual real labour
productivity growth between 2004 and 2007. \*\*\*, \*\* and
\* denote significance at the 1 percent, 5 percent and
10 percent level. t-values are based on cluster-robust standard errors
with 219 clusters (by industry and country). Sector and country dummy variables
are included but not reported.
Source: AMADEUS firm-level database.

Table A.8: OLS estimates of the
impact of FDI on average employment growth 2004-2006, 8 EU-10 countries

|| Foreign presence based on inward FATS

|| Horizontal || Backward

|| coeff || || t || coeff || || t

foreign presence in the same industry in 2003 (FOR03) || 0.08 || \*\*\* || 2.68 || 0.04 || || 1.62

foreign presence in customer industries in 2003 (FORCUST03) || 0.03 || || 0.90 || 0.10 || \*\* || 2.41

employment growth of foreign affiliates 2004-2006 || 0.10 || \*\*\* || 5.72 || 0.11 || \*\*\* || 5.69

ln sales per employee of local firms to that of foreign firms, 2004 || 0.03 || \*\*\* || 7.02 || 0.03 || \*\*\* || 8.68

ln sales per employee of local firms to that of foreign firms, 2004 X (FOR03) || 0.07 || \*\*\* || 3.62 || || ||

ln sales per employee of local firms to that of foreign firms, 2004 X (FORCUST03) || || || || 0.09 || \*\* || 2.40

ln employment in 2004 || -0.46 || \*\*\* || -21.67 || -0.46 || \*\*\* || -21.71

ln employment squared in 2004 || 0.04 || \*\*\* || 16.61 || 0.04 || \*\*\* || 16.58

country and industry dummies || yes || || || yes || ||

Constant || 0.94 || || 7.83 || 0.95 || \*\*\* || 7.48

R2 || 0.447 || || || 0.45 || ||

number of observations || 37,893 || || || 37,893 || ||

average effect of FOR2004 || 0.12 || \*\*\* || || || ||

average effect of FORCUST2004 || || || || 0.15 || \*\*\* ||

|| Foreign presence based on CIS 2006 ||

|| coeff || || t || coeff || || t

|| Horizontal || backward

foreign presence in the same industry in 2004 (FOR04) || 0.08 || \*\*\* || 2.70 || 0.05 || \*\* || 2.09

foreign presence in customer industries in 2004 (FORCUST04) || 0.04 || || 1.55 || 0.08 || \*\*\* || 2.70

employment growth of foreign affiliates 2004-2006 || 0.11 || \*\* || 5.85 || 0.11 || \*\*\* || 6.02

ln employment in 2004 || -0.46 || \*\* || -21.68 || -0.46 || \*\*\* || -21.71

ln employment squared in 2004 || 0.04 || \*\*\* || 16.59 || 0.04 || \*\*\* || 16.59

ln sales per employee of local firms to that of foreign firms, 2004 || 0.03 || \*\*\* || 8.38 || 0.03 || \*\*\* || 7.00

ln sales per employee of local firms to that of foreign firms, 2004 X (FOR03) || 0.04 || \*\* || 2.37 || || ||

ln sales per employee of local firms to that of foreign firms, 2004 X (FORCUST03) || || || || 0.06 || \*\* || 2.40

country and industry dummies || yes || || || yes || ||

Constant || 0.93 || || 8.27 || 0.94 || \*\*\* || 8.28

R2 || 0.446 || || || 0.45 || ||

number of observations || 37,8966 || || || 37,8966 || ||

average effect of FOR2004 || 0.09 || \*\*\* || || || ||

average effect of FORCUST2004 || || || || 0.11 || \*\*\* ||

Note: \*\*\*, \*\*, \* denote statistical significance at the
1 percent, 5 percent, and 10 percent level. Standard errors are computed using robust standard errors
clustered on industry-country pairs. FORCUST03 and
FORCUST04 measure the backward linkage from foreign-owned firms to domestically
owned firms.
Source: Inward FATS, CIS (2006).

Table A.9: Probit estimates of the
impact of FDI on technological innovations of local firms 2004-2006, 8 EU-10
countries (marginal effects)

|| (i) || (ii) || (iii)

|| marg eff || || z || marg eff || || z || marg eff || || z

|| Dependent variable: probability of introduction of new market products of local firms

introduction of new market products of foreign firms || 0.04 || \*\* || 3.13 || 0.04 || \*\*\* || 3.37 || 0.04 || \*\*\* || 3.11

foreign presence in the same industry 2004 (FOR04) || -0.01 || || -0.80 || -0.02 || || -0.95 || 0.00 || || -0.06

foreign presence in customers industries in 2004 (FORCUST04) || 0.04 || || 1.53 || 0.06 || \*\*\* || 2.56 || 0.04 || || 1.52

ln RELPROD04 || 0.01 || \*\*\* || 4.99 || 0.00 || || 0.47 || 0.01 || \*\* || 2.34

ln RELPROD04 X (FOR04) || || || || || || || 0.02 || \* || 1.94

ln RELPROD04 X (FORCUST04) || || || || 0.04 || \*\* || 2.34 || || ||

ln employment || 0.00 || || -0.07 || 0.00 || || -0.07 || 0.00 || || -0.07

ln employment squared || 0.00 || \*\* || 4.92 || 0.00 || \*\*\* || 4.91 || 0.00 || \*\*\* || 4.93

country and industry dummies || yes || || || yes || || || yes || ||

number of observations || 37866 || || || 37866 || || || 37866 || ||

Pseudo R2 || 0.12 || || || 0.12 || || || 0.12 || ||

|| Dependent variable: probability of introduction of new product innovations of local firms

|| marg eff || || || marg eff || || z || marg eff || || Z

introduction of product innovations of foreign firms || 0.05 || \* || 1.75 || 0.05 || \* || 1.90 || 0.05 || \* || 1.74

foreign presence in the same industry 2004 (FOR04) || -0.03 || || -1.00 || -0.04 || || -1.12 || 0.00 || || 0.04

foreign presence in customers industries in 2004 (FORCUST04) || 0.08 || \* || 1.73 || 0.13 || \*\*\* || 2.68 || 0.08 || \* || 1.71

ln RELPROD04 || 0.02 || \*\*\* || 5.49 || 0.00 || || 0.06 || 0.01 || \* || 1.83

ln RELPROD04 X (FOR04) || || || || || || || 0.05 || \*\*\* || 3.47

ln RELPROD04 X (FORCUST04) || || || || 0.08 || \*\*\* || 3.08 || || ||

ln employment || -0.01 || || -1.13 || -0.01 || || -1.16 || -0.01 || || -1.15

ln employment squared || 0.01 || \*\* || 7.70 || 0.01 || \*\*\* || 7.74 || 0.01 || \*\*\* || 7.75

number of observations || 37866 || || || 37866 || || || 37866 || ||

Pseudo R2 || 0.10 || || || 0.10 || || || 0.10 || ||

|| Dependent variable: probability of introduction of new production processes of local firms

|| marg eff || || z || marg eff || || z || marg eff || || Z

introduction of new production process of foreign firms || 0.05 || \*\* || 2.26 || 0.05 || \*\* || 2.37 || 0.05 || \*\* || 2.25

foreign presence in the same industry 2004 (FOR04) || -0.02 || || -0.91 || -0.03 || || -1.05 || 0.01 || || 0.24

foreign presence in customers industries in 2004 (FORCUST04) || 0.05 || || 1.26 || 0.11 || \*\* || 2.49 || 0.05 || || 1.24

ln RELPROD04 || 0.02 || \*\*\* || 6.61 || 0.00 || || 0.23 || 0.01 || \*\*\* || 2.72

ln RELPROD04 X (FOR04) || || || || || || || 0.05 || \*\*\* || 2.69

ln RELPROD04 X (FORCUST04) || || || || 0.10 || \*\*\* || 4.10 || || ||

ln employment || -0.02 || \*\* || -2.32 || -0.02 || \*\* || -2.33 || -0.02 || \*\* || -2.32

ln employment squared || 0.01 || \*\*\* || 9.13 || 0.01 || \*\*\* || 9.07 || 0.01 || \*\*\* || 9.11

country and industry dummies || yes || || || yes || || || yes || ||

number of observations || 37866 || || || 37866 || || || 37866 || ||

Pseudo R2 || 0.09 || || || 0.10 || || || 0.10 || ||

Note: \*\*\*, \*\*, \* denote statistical significance at the
1 percent, 5 percent, and 10 percent level. Standard errors are
computed using robust standard errors clustered on industry country pairs.
FORCUST04 measures the backward linkage from foreign owned firms to domestically
owned firms.
Source: Inward
FATS, CIS (2006).

Figure A.1 - 
Revealed comparative advantages in EU-27 FDI relations with the rest of the
world

EU stocks are stocks of the EU-27
Aggregate. Total inward stocks exclude the inward stocks of the finance
industry (EU nomenclature: 6895, financial intermediation). RCAs in industry i
is calculated as . OFDI are EU outward stocks and IFDI are
EU inward stocks.

Source: Eurostat, wiiw-calculations.

5. Clusters and
Networks

              5.1. Introduction

Academics and policy makers have been interested for a long
time in linkages between companies that go beyond market interactions, but that
fall short of vertical. Thus, the issue of clusters and networks of firms is not
recent. What has changed, however, is that globalisation and new types of
innovation processes have over the last few decades reshaped in new ways the organisation
of value chains. Activities that were traditionally provided within a firm are
now provided in a different type of institutional setting, somewhere between
hierarchy and market.

In the global economy, there is a growing interest in new organisational
structures, which are flexible enough to respond to market changes and at the
same time solid enough to take on cooperative projects. In this sense, the
increasing amount of statistical evidence indicating a positive relationship
between the presence of clusters and the prosperity of regional economies[48]
has brought to the fore the positive role that clusters and networks could
play. Clusters and networks are increasingly seen as catalysts for accelerating
industrial transformation and for developing new regional competitive
advantages, speeding up the creation of firms and jobs and thereby contributing
to growth and prosperity.

Because of these characteristics, clusters and networks have
been identified as crucial instruments for implementing the EU's Europe 2020
strategy. The EU 2020 flagship initiatives ‘Innovation Union’ and ‘An
integrated industrial policy for the globalisation era’ specifically refer to
clusters and networks as critical tools.

Over the last few years, the European Commission has
supported a range of research and joint learning efforts. It has also set up
specific advisory bodies that have analysed in detail the presence of clusters
across Europe and the potential for policy, especially policy at EU level, to
leverage them and strengthen their growth. Many of these activities, including
the European Cluster Observatory, the European Cluster Alliance, the European
Cluster Excellence initiative, the TACTICS group and the European Cluster
Policy Group, have been organised under the Competitiveness and Innovation
Programme (CIP). These activities have informed a number of Commission
communications, policy documents, and action agendas on clusters.

While it is relatively easy to detect and assess the
presence of clusters and their economic impact, networks are more elusive. On
the one hand, the theoretical literature on networks is less developed than in
the case of clusters, leading to many conceptual misunderstandings. On the
other hand, there is a relative scarcity of empirical evidence, since a company
that decides to participate in a network may be extremely reluctant to disclose
any information for fear of exposing its competitive advantage to its rivals.

This
chapter is specifically focused on the presence and role of firm networks and
their potential as a tool or platform for EU programmes to enhance
competitiveness. It aims to inform the debate as to whether network-oriented
policies are a substitute, a complement or an instrument in relation to
cluster-based economic policies and to clarify the role of the European
Commission in this this.

To this
end, the chapter is structured as follows. The first section contains
operational definitions to distinguish clusters from networks. The next section
discusses the presence of networks in the EU, as well as the public programmes
and tools, which support networks. Then, the following section deals with the
rationale, objectives and design of network-support programmes. Finally, the
last section sums up the policy implications.

              5.2 Concepts of Clusters,
Cluster organizations and Networks

The
term ‘cluster’ has a long tradition in economics. At the end of the nineteenth
century Alfred Marshall had already observed the ‘concentration of specialised
industries in particular localities’. For policy-makers, too, the phenomenon of
industries moving into the same geographical area has not gone unnoticed. In
fact, a number of countries have viewed the investment of state aid into
specific territories as a means of embedding an industry into a targeted region
with a view to fostering growth and development.

Over
the last decades, the literature on firm networks has grown alongside cluster
studies, with a similar emphasis on linkages among companies. However, the
networks literature is not so much concerned with the concentration of firms in
particular areas, but rather with the process that leads individual firms to
establish cooperative links with each other, even if they operate in different
regions.[49]

Clusters
and networks share some common features. Conceptually, both are located between
the atomistic structure of an uncoordinated market and the organic structure of
a vertical hierarchy. Firms within networks and clusters are linked by
something more than the price mechanism of the market. However, they are not
branches of a larger company, since they continue to be independent.

In
spite of these similarities, it is very important to draw a line between them,
all the more so since focusing on clusters or networks has very different
policy implications. In the case of clusters, the rationale for state
intervention is clearly derived from the presence of externalities. Regardless
of managers´ intentions, externalities create knowledge spillovers, affect the
dynamics of rivalry, and encourage the development of a more specialised labour
market and supplier base. Hence, governments can help cluster organisations
internalise some of the externalities in clusters by promoting joint
decision-making and action and can also organise funding programmes around
clusters to compensate for externalities.

On the
other hand, the presence of externalities in networks that spread across
different regions is not so obvious. The crucial point is the activity in which
firms are engaged. If a group of firms is working on innovation projects or
entering new fields or new markets, companies could be encouraged to join a
network structure for the purpose of sharing information and creating
synergies.

Therefore,
conceptual categorisation is required. This chapter employs the following
operational definitions in order to clarify the conceptual relations and
differences between clusters, cluster organisations and networks.

Clusters
are geographically co-located firms and other institutions engaged in economic
activities in a set of related industries, connected through externalities and
other types of linkages. Collaboration may or may not take place, and could
focus either on broader competitiveness upgrading or on specific projects.

Cluster
organisations are organisations focused on a specific
geographical area, oriented towards a set of related industries (also called a ‘cluster’
category), and they provide a structure for actual collaboration.

Networks
of firms are structures specifically created for active collaboration. This
collaboration could be open-ended or focused on a specific project task. They
may or may not be confined to a specific geographical location and set of
industries. Cluster organisations are a specific type of network that is
concentrated in a particular geographical area.

Figure
5.1: Key characteristics of clusters, cluster organisations, and networks

              5.3. Presence and Policy
of Networks

              5.3.1. Types of Firm
Networks

While
the presence of clusters is quite easy to detect, the presence of networks is
more problematic. As mentioned in the previous section, networks are created on
a voluntary basis, because firms expect it to be more advantageous to stay in
the network than to stay outside it. Thus, it is in firms' interests to be
discreet about their participation in a network for fear of revealing sensitive
information from which their rivals might benefit.

Nevertheless,
useful information about networks can be found in the organizational database of
the European Cluster Observatory (ECO), a site developed with financial support
from the European Commission. This
database covers more than 2000 organizations[50]
in total with a focus on economic development through collaboration between
firms and other entities and has been created partly through internet search
and partly through self-registration by organisations.

Of all
the organisations covered by the ECO database the percentage of organisations
that could be defined as networks in the terms specified above is between 4-6 %.
If the analysis is restricted to particular categories of activities, it turns
out that in areas such as ‘general technology’, ‘design’ or ‘human resources’,
the network share is even higher and reaches 10-12 % in life sciences
(biotech/pharmaceuticals).

On the
basis of these findings, two criteria (geographic scope and industry scope),
can be put forward for the purpose of classifying networks.

Since networks are not constrained to a specific
geographical area and can involve firms operating in regions which are quite
far apart, geographic scope could be an instrument for classifying and
systematising networks. Thus, in terms of their geographical extension,
networks could be classified from the most locally concentrated to the most
geographically scattered.

·
The first type of networks takes place at
regional level. They aim at favouring the exchanges of information and
experiences. An example is the Romagna Creative District in Italy (see Annex
Box 5.2) that aims at creating synergies between twelve different creative
sectors.

·
The second type of networks are those open to
membership from a broad set of regions within a country. These networks
tend to be set up to overcome a lack of critical mass at regional level. The
networks of the German Kompetenznetze.de,[51] a federally funded
network of clusters or networks, are a good example.

·
The third type refers to networks operating in a
set of similar industries and that organize themselves explicitly at the national
level. In general, they are set up by government to compensate for a lack
of critical mass at the regional level and create a cost-efficient central
platform to provide services for firms in the same industrial activity. Such
networks exist, for example, in Ireland (Irish Software Innovation Network),
the Netherlands (Dutch Maritime Network), and Slovenia (Technology Network
ICT).

·
The fourth type of networks extends beyond
national boundaries and connects firms that work in a set of related
industries, in most cases through participation in cluster organisations. This
happens either across smaller countries or in response to EU-funded projects
driving the emergence of European networks. One such network is Scanbalt, which
focuses on life sciences in the Baltic Sea Region, is such a network (see Annex
Box 5.1).

·
Finally, the last type of network is formed by
firms which pursue one specific issue and find that it is in their interest to
try to operate at EU level. This is the case of Social Firms Europe
CEFEC (see Annex, Box 5.4), a network of social firms and cooperatives across
Europe, whose goal is to create paid work for disabled and disadvantaged people
and help individuals who face discrimination in their bid to overcome their
social and economic exclusion through employment. CEFEC is open to all
industries that can help people with disabilities or disadvantages find
employment.

In addition to geographic coverage, industry scope could
provide other useful criteria for classifying networks.

·
The first type of network focuses on new emerging
patterns of relatedness across industries. Networks in this category are
often strongly driven by government action to explore the potential of new
fields. One such effort is the Romagna Creative District in
Italy (see Annex, Box 5.2) whose
aim is to connect and share the creative resources of individuals and companies
in the hope of sparking off creativity and boosting the economy of the Romagna
region. The network covers creative sectors such as communications, art,
design, architecture, theatre, music and photography.

·
The second type of network covers a broader
set of industries, often in wider traditional sectors such as
manufacturing. Those networks have a broader industry-scope than one cluster
category. An example is the Network Industry RuhrOst
(NIRO), which aims to enhance the competitiveness of firms in mechanical
engineering and industrial electronics located in the RuhrOst region around the
cities of Dortmund and Unna. This type of network is in
response to a lack of critical mass for firms working within similar industries
within a region.

·
The third type of network aims to enhance the
competitiveness of the entire regional economy. The Cambridge Network in
the UK falls into this category. Its purpose is to connect
people from business and academia in the Cambridge region in order to share
ideas, thereby encouraging collaboration and partnership that can contribute to
the overall economic success of the region. Although some activities are often
directed towards a cluster-orientation, others aim to improve the general
business environment.

              5.3.2. Public Policy
Support to Networks

For several reasons, regional administrations, national
governments and supra-national institutions have designed programmes aimed at
strengthening clusters and networks. Although the scope, ambition and
achievements of these programmes depend on their political, geographical and
administrative context, public authorities have a common interest in fostering
cooperative links between firms. These programmes do not target networks or
clusters per se, but tend rather to focus on activities with a positive
impact on a wider community. Since clusters are easier to identify and there is
a longer policy tradition of working through them, in most cases network
programmes are a part of existing cluster programmes. Policy makers who
decide to give a special boost to networking, do so because regions lack
critical mass or because there is a case for supporting collaborative projects,
such as joint research or education.

In the previous subsections networks were classified
according to their geographic or industrial focus and these two criteria
continue to be relevant for the purpose of classifying public network
programmes.

              5.3.2.1. Geographic focus.

Programmes for networks that have a different geographic
focus have been launched by some larger regions, national governments, and
as part of cross-national collaboration.

A number of larger German states have organised region-wide
cluster efforts (‘Bayern Innovative’, ‘bwcon’, ‘bw-automotive’,
‘Landescluster NRW’). All clusters belonging to the same industry are served
through one network organisation, either driven directly by government or
through a company that drives it on behalf of government. This seems to be
partly a reflection of limited critical mass in smaller regions and partly a
matter of political and organisational expedience in aligning the organisation
with the way the public sector is organised.

Countries like France (‘Action Collective’), Germany
(‘ZIM-NEMO’), and the Netherlands (‘Innovation Performance Contract’) have
launched programmes at national level that invite groups of companies to
apply for funding to set up a network. All these programmes are focused on
enhancing the performance of groups of small- and medium-sized enterprises
(SMEs), mostly by encouraging joint innovation activities but sometimes also
joint exporting efforts. Co-location in one specific region is not a criterion
for funding. Unlike traditional cluster programmes, the motivation for these
networks is, at least initially, a specific task or objective that can best (or
only) be achieved collectively. Over time, however, these programmes hope to
encourage more stable patterns of collaboration that are then motivated by a
broad common interest in upgrading the competitiveness of the firms in the
network.

The Italian programme in support of contract-based business
networks (‘Contratto di Rete d’Impresa’) is similar to this approach but is
also open to large companies and seems to be less restrictive in terms of the
type of joint activities that qualify for support. It provides tax incentives
for collaboration, often among small groups of around five companies that frame
some of their activities within a specific legal structure.

Countries like the UK (‘Knowledge Transfer Networks’),
Ireland (‘Irish Software Innovation Network’), the Netherlands (‘Dutch Maritime
Network’) and Slovenia (‘Technology Network ICT’) have set up national
platforms serving specific cluster categories. In some ways, these
platforms are natural extensions of traditional industry- or sector-oriented
programmes in research and innovation policy. The platforms, largely financed
by government, provide companies with information on how to access project
funding from other parts of government. While this funding might be based on
collaboration, the networks also provide information about more traditional
firm-based programmes. In addition, the networks aim to encourage linkages
between firms and research institutions carrying out a set of similar
industrial activities to increase the effectiveness of the research funding.
The networks also provide additional information on industry and technology
trends to enhance companies' overall sophistication.

National networks in
Denmark (‘Innovation Networks Denmark’) and Finland (‘OSKE Centre of Expertise
Programme’) have been strengthened thanks to a base of regional cluster
efforts. As these efforts proved to have insufficient critical mass, the
national government consolidated them under a country-wide umbrella. Where
robust regional clusters exist, they continue to play an important role. The
national approach explicitly aims to connect firms which are active within
these cluster categories but located in other regions within the country.

The EU and groups of EU neighbouring countries have also set
up several programmes to encourage the emergence of networks across larger
geographical areas. In almost all cases, these networks are facilitated
through regional cluster organisations. The Knowledge and Innovation
Communities (KICs) are one such example at EU level. The available funding
combines networking and actual research activities. In the Baltic Sea Region,
the StarDust programme has been launched as part of the EU Baltic Sea Region
Strategy to connect regional clusters across the wider Region in five cluster
categories. Funding is available for network management between the cluster
organisations, while collaborative actions, including networking between firms
in the regional clusters, have to be covered through the existing budgets of
the cluster organisations.

              5.3.2.2. Industry focus.

Support for network organisations that have a different
industry focus from traditional cluster categories is to a large degree
organised through the same type of network programmes discussed above. While
the general toolkit is the same, in these cases government agencies decide to
change the scope of the network.

A number of governments have set up specific network
programmes in areas considered to be emerging, where activity boundaries
are porous. In the UK, the Creative Industries Network, part of the Knowledge
Transfer Networks, focuses on the broad range of industries designated as
‘creative’ in the academic literature and increasingly also in policy
programmes. In Austria, the regional economic development agency supports
networks in nanotechnology, nanosciences, and creative industries as part of
its overall cluster and network programme. In Denmark, Environmental Network
South (See Annex, Box 5.3) focuses on the collaboration between public
authorities and companies in the area of the environment.

A number of governments at the local and regional level,
especially in Germany, support SME networks that reach out to local companies
in broad sectors such as manufacturing. In such cases the main
motivation is to create cost-effective tools, to have large numbers of
companies improve their operational sophistication and to establish platforms
for communication between local government and the local business community.

When the goal is to support the overall competitiveness
of a region, networks are usually not funded by government. This task tends
to be undertaken by regional economic development agencies set up by regional
authorities, working in dialogue with the business community they serve. In
Germany, economic development organisations such as HannoverImpuls and the
Dortmund-Project arose from specific projects that aimed to reframe the way
local government pursued its economic development efforts.

              5.3.3. Public Tools

Many
programmes use financial incentives to encourage collaboration. Some pay only
for network management activities. Others make funding for, say, joint
innovation activities, conditional on the presence of a network. Compared to
traditional cluster programmes, the funds in network programmes tend to be much
smaller. There is more focus on networking activities, joint activities are often
smaller in scale, and the number of participants also tends to be significantly
lower than in cluster programmes. An interesting new effort currently being
tested in France is ‘Territoires et innovation’, a programme that supports
regional networks ‘in kind’, through consulting services and by providing
access to bank credit, the aim being to support the export activities of SMEs.
There is no direct financial support for the SMEs involved.

One group of programmes provides funding and then invites
prospective networks to submit their proposals. This approach is used when
there is no clear information or political target in terms of the type of
networks to support, and when collaboration between firms is the prime
objective. A different group of programmes defines the network scope and then
sets up an organisation to mobilise, serve, and manage the network of firms.
This organisation can be part of government, or it can be run by another
organisation on behalf of government. This second approach is more interventionist,
with the focus areas selected by government. However, in setting up an
intermediary linked to both firms and government, the available policy tools
and programmes of government are also more likely to be linked to the needs of
a set of companies.

An interesting development is the emergence of national
support mechanisms for all clusters and networks within a country. In Denmark,
RegX, RegLab, and netmatch provide different types of training and information
services to the country's innovation networks. In Austria, the national cluster
platform has been created to enable collaboration between the clusters and
networks that have developed through the initiative of regional governments. In
Germany, Kompetenznetze.de provides a national platform bringing networks
together to collaborate and learn about best practices. In the German state of
North Rhine –Westphalia, a central cluster secretariat supports all the
clusters and networks in the state.

In terms of impact, the evidence relating to network
programmes is limited. Available evidence does suggest that companies
participating in collaborative research efforts, i.e. those facilitated by
network programmes, record better results on a number of key indicators than
peers that do not belong to such networks.[52]
Evaluating the effect of these programmes raises difficult questions.
Particularly difficult to disentangle is whether the superior performance of
network-participating companies is due to the programme itself or to unobservable
individual characteristics. While evaluations of such programmes tend to
provide fairly positive assessments, there is hardly any hard impact data
available.

              5.4. The Role of Public
Policy

Since economic resources are scarce, public policies must be
carefully designed to avoid wasting time and money. Likewise, it is crucial
that design programmes are not taken over by special interest groups to the
detriment of the public good. Hence, every proposal relating to a public policy
programme must address three issues: first, its rationale; second, its
objectives; and third, its operational design.

              5.4.1. Justification of
network programmes.

The first question to ask is whether there is a good case
for public policy. Public policy interventions should be based on a clear
social welfare argument. In the case of cluster organisations, such an argument
is founded in the existence of local externalities that give rise to the
emergence of a cluster and drive cluster dynamics. There is a market failure
that government intervention can address.

One way of doing this is to internalise the externality by
creating an organisational structure that allows members of the cluster to
share information and coordinate action. Government can play a role in
initiating and supporting this organisational structure, i.e. a cluster
organisation. Interestingly, if the argument for government support is an
externality, some government engagement is reasonable as long as the
externality exists. In this case, there is no fundamental reason for governments
to finance cluster organisations only in the start-up phase. Expanding the
range of activities, however, should be driven by private sector contributions.

Another way of doing this is for government to compensate
for the externalities by providing government funds to support the specific
activities that create them. This can be done by organising public policies in
areas such as innovation, workforce development, and investment attraction
around clusters. This approach also has key operational advantages in
comparison with programmes that target individual companies or, conversely, the
entire economy. On the one hand, they are more effective because they reach a
larger group of companies than firm-level support but are more targeted than
economy-wide programmes. On the other hand, they create less distortion than
firm-level support, because they include all industries that are active along a
value chain and compete for the same specific inputs.

The welfare argument for public support to networks is more
complex. There is no inherent externality, and thus no generic argument, for
funding networks. There are, however, two arguments that can support public
network programmes. First, the externalities might occur at the level of the
activity that the network is engaged in. If, for example, networks work on
collaborative innovation projects, collaborate in projects that explore the
potential of emerging new fields, or collaborate on export efforts towards a
new market, there could be knowledge spill-overs that justify public support.
Second, the network might be a more efficient delivery tool for public
investments in knowledge provision, largely because a large number of companies
can be reached through a common platform. In both cases, then, the argument for
networks rests on what they do, not on the network per se.

One example of a network activity that can provide
significant positive externalities is that of exports towards a new market. The
statistical evidence shows that entering a new market is a risky endeavour and
that most such attempts fail.[53]
As the information needed to evaluate the potential of a new market is often
dispersed, this is where a network can help. Once an attempt has been made to
enter a new market, the revealed evidence of success or failure provides
valuable information to other companies considering a similar move. This is why
public support to cover some of the risk can be justified. The same logic might
apply to emerging industries, where new combinations of technologies and
operational practices are used to meet (potentially new or changing) customer
needs. Rather than just subsidising the search activity, that is the entry into
a new market, public support for networks can lower the search costs and make
the search activity more efficient.

Network programmes that support collaboration between
companies but impose little conditionality on the actual activities within the
network are hard to justify. They provide public subsidies to a small group of
companies to conduct activities that mainly generate private benefits for them.

              5.4.2. Objectives of
network programmes.

Thus, the second question to be addressed relates to which objectives
network programmes should have, in other words, in which situations are network
programmes useful additions to the public policy toolkit. This discussion will
focus on network programmes that are separate from the networking activities
supported as part of traditional cluster programmes.

In the light of experience there are four types of network
programmes that seem to complement existing cluster programmes particularly
well. First, networks with a broader geographic and industry scope than
established regional clusters can play a useful role in the early stages of
cluster development, including work with emerging industries. Networks can
then be an important element in an integrated cluster policy that recognises
the different needs of clusters throughout the cluster life cycle.[54]
In existing cluster categories, new regional clusters might not have reached critical
mass. Networks can then be a flexible tool to help companies collaborate and
explore growth opportunities. They allow firms to tap more easily into
complementary capabilities of companies located elsewhere. In emerging cluster
categories, networks can be a tool for companies to explore opportunities for
new markets to emerge by recombining technologies and capabilities from
traditionally different cluster categories. They allow them to act more easily
across cluster boundaries.

Second, networks can provide shared services and connect
individual firms from weaker regional clusters across a larger region or
nation. This amounts to a more efficient use of public support infrastructure
in terms of knowledge provision and sharing. Moreover, it helps to overcome the
challenges of limited critical mass in individual regions. However, this is
always a second-best solution compared to allowing companies to agglomerate and
regions to specialise more strongly. Given the considerable barriers to
mobility that still exist in Europe, some of them policy-made but others
related to culture and behaviour patterns, these national networks can play a
useful role, even if cluster dynamics will inherently be more limited than in
the case of a strong regional cluster.

Third, networks can be a useful tool for organising
activities specifically directed towards SMEs. The importance of SMEs is
increasing in both exports and innovation processes. Nevertheless, their needs
for public support in these activities are different from those of large
companies that have been the traditional focus of policy in these areas (and
that continue to play a dominant role in them). Network programmes can be an
efficient tool for reaching out to a larger number of SMEs without creating
unmanageable process costs. In some cases these networks will be separate from
clusters. Here the network is a mechanism to improve the general sophistication
of SMEs in activities that have significant fixed costs or create positive
externalities. In other cases, the SME network will be part of a cluster.[55]
Here the network can be connected to large companies that in turn provide
connections to global value chains and distribution channels.

Fourth, networks can be a useful tool for more comprehensive
efforts to enhance regional competitiveness. The focus on these networks
might be on clusters, where there is sufficient critical mass. If this is not
the case, networks can focus on cross-cutting framework conditions that are
relevant across a broader range of industries and clusters. The network is then
an efficient platform for information exchange and dialogue, providing a
connection to local and regional authorities to companies that otherwise would
not have access.

              5.4.3. Operational design
of network programmes.

The third question concerns the operational design of
network programmes. Here the evidence is still limited but the analysis
suggests a number of issues for consideration.

First, network programmes should set out clear objectives
for the actual activities of the network. Collaboration does not happen
automatically, even if some funding is provided. Without clear targets there is
a danger that network programmes attract what have become known as ‘hunting
parties’, i.e. small groups of companies, often facilitated by a consultant,
that tap into available funding without creating any meaningful public value.
Given the modest budgets required for network programmes, there is a danger of
wasting money on numerous small efforts without any clear impact.

Second, network programmes should be managed on the basis of
clear milestones with a transparent exit strategy for networks that do
not meet expectations. For cluster organisations supporting established
clusters there is a case for providing predictable long-term funding for
connections to emerge. For networks operating in more fluid environments with a
much higher likelihood of failure, it is more important to keep reviewing and
pruning the portfolio of supported networks. It should be easier to obtain
support but also easier to lose it.

Third, network programmes should make significant use of
in-kind services rather than direct financial support. What is missing in
networks is the structure to collaborate and the knowledge to provide through
these structures, rather than capital (that in clusters is designed to
compensate for externalities). Providing funds to buy these services rather
than having the services provided directly by government may have a negative
impact on incentives and can in some cases be less efficient. In this context
the national support units for networks and clusters are an interesting recent
innovation.

Fourth,
network programmes designed for emerging clusters should be integrated into an overall
programme for cluster support. There needs to be a clear transition to the
next stage of the programme, reflecting the changing needs of clusters as they
evolve and providing incentives to be assertive in pursuing the development
from a network to a cluster organisation.

              5.5. Policy implications

The analysis
of existing public policy programmes to support or leverage firm networks
reflects a wide range of approaches, driven to a large degree by the
significant differences in size, government structure, and economic profile
across European countries. Some network programmes are closely connected to
clusters and cluster organisations, focusing on clusters that have only
regional importance, or connecting regional clusters within a national
structure. Others are less like clusters, especially those that support
networks of SMEs in specific activities such as innovation or exports. In
particular, they have a different geographic and industry scope.

Public support for network programmes can be motivated by
the activities that the network organises and by the efficiency of the network
as a policy delivery channel. Unlike clusters, the nature of the network itself
is not a reason for intervention. There are three types of network programmes
that have the highest potential to add useful instruments to the policy toolkit
for economic development:

·
support for networks in emerging industries and
clusters;

·
establishment of national cluster platforms to
provide shared services and connect firms across regions;

·
support for networks of SMEs active in areas
with positive externalities, such as innovation and exporting to new markets.

Many
networks are market driven and hardly require any policy intervention.
Nonetheless, proper framework conditions are essential if private organisations
are to have the incentives to invest in networks. Europe-wide network
programmes are a useful complement to cluster-based programmes. Moreover, if
intervention is to take place, in-kind services should be preferred to direct
financial support. The objectives and operational design of network programmes
are to be carefully thought through and implemented to reap the expected
benefits. If clear milestones are identified early on, the network programmes
can be monitored. It should be possible to discontinue unsuccessful programmes.

 
References

Bathelt, Harald, Anders Malmberg, Peter Maskell
(2002), Clusters and Knowledge: Local Buzz, Global Pipelines and the Process
of Knowledge Creation, DRUID Working paper 02-12, DRUID:  Copenhagen.

Danish Agency for Science, Technology, and
Innovation (2011), Innovation Network Denmark Performance Accounts 2011,
DASTI: Copenhagen.

Delgado, Mercedes, Michael E. Porter, Scott Stern (2011), Clusters,
Convergence, and Economic Performance, Census Working Paper available at www.isc.hbs.edu

DG Enterprise and
Industry (2007), Innovation Clusters in Europe, DG Enterprise and
Industry: Brussels.

Hausmann, Ricardo, Dani
Rodrik (2002), Economic Development as Self-Discovery, NBER working
paper 8952, NBER: Cambridge.

Ketels, Christian
(2012), The impact of clusters and networks of firms on EU competitiveness –
Background Report, DG Enterprise and Industry: Brussels.

Ketels, Christian (forthcoming 2012), Cluster Policy: A
guide to the state of the debate, in: Knowledge and Economy, Springer
Publishing.

Frank Learch and Gordon
Müller-Seitz (2012), Literature review: Interorganizational (firm) networks
in Ketels (2012).

Lindqvist, Göran, Örjan
Sölvell (2011), Organising Clusters for Innovation: Lessons from City
Regions in Europe, Europe Innova: Brussels.

NGP Excellence (2012), The
Perfect Cluster Programme, VDI-VDE, draft version, Berlin: May 2012.

Sölvell, Örjan,
Christian Ketels, Göran Lindqvist (2003), The Cluster Initiative Greenbook,
Ivory Tower: Stockholm.

ANNEXES

Box 5.1 Case-study on
cross-national network based on regional clusters: Scanbalt, Baltic Sea Region

Scanbalt
(http://www.scanbalt.org)
promotes the development of ScanBalt BioRegion as a globally competitive
macro-region and innovation market within health and life sciences. ScanBalt
promotes projects, business and research, visibility and branding, policy
issues, regional innovation and cluster development. The
network is active in the Baltic Sea Region comprising
Denmark, Estonia, Finland, Iceland, Latvia, Lithuania, Norway, Poland, Sweden, the
northern part of Germany and the north-western part of Russia. ScanBalt
BioRegion also collaborates with neighbouring regions of particular interest,
e.g. northern Netherlands. It includes the health and life science community
and related industries.

Scanbalt
has two co-opted founding members (Nordic Innovation Centre, Nordforsk), 26
founding members, 19 institutional members, and two affiliated members. Any
public or private organisation involved in life sciences can apply for
membership (if located in the ScanBalt BioRegion) or affiliated membership (if
located outside the ScanBalt BioRegion). The cost of membership fees depends on
the membership type (here 2011 prices). Founding members (FOU) pay EUR 5,500
per annum and have five votes in the General Assembly and one vote in the
Executive Committee (ExCo). Institutional members (INS) pay 1,100 EUR per annum
and have one vote in the General Assembly; if elected to ExCo, INS also have
one vote there. Affiliated members (AFF) pay 1,100 EUR per annum and have
similar voting rights as institutional members. Affiliated members may apply
for founding membership if they receive a corresponding invitation from ExCo.

The
Scanbalt secretariat is located in Copenhagen with liaison offices in Tartu,
Gdansk, Groningen and Copenhagen. There is one person working full-time in the
secretariat in Copenhagen, who is the only person financed directly by
ScanBalt. Other secretariat members work in the liaison offices and are
regionally financed. The General Assembly (GA) is the network’s highest body;
it decides upon the change of statutes or membership fees and advises ExCo on
the association’s strategy. The Executive Committee (ExCo) decides on all relevant
matters that do not require GA’s approval. ExCo comprises of Founding Members,
up to 6 Institutional Members and up to 5 Co-Opted Members of strategic
interest. Scanbalt’s Chairmanship w is responsible for representing of the organisation
and overseeing the management. The Chairmanship comprises a Chairman elected by
ExCo and up to 4 Vice Chairmen proposed by the Chairman and approved by ExCo.
The term of Chairmanship is 2 years with the possibility of being re-elected
twice. Scanbalt’s annual budget is about DKK 1,500,000 or EUR 200,000. However,
this only covers the budget of the CPH secretariat; there is much more
financing for regional liaison offices and actual activities. The budget is made
up of 50 % fees and 50 % external resources (CPH secretariat only). Over the
last decade about EUR 20 M of EU funds were used for specific activities in
research and education.

The
ScanBalt BioRegion project was piloted and then initiated in full in 2002 by
the Nordic Innovation Centre and the Nordic Council of Ministers. In 2004
ScanBalt became an independent legal entity, a non-profit membership
association (ScanBalt fma). The year 2005 saw the establishment of the ScanBalt
Academy which started organising ScanBalt Summer Schools in 2008 and became an
independent non-profit association in 2011. In 2006 ScanBalt became a strategic
partner of the Council of the Baltic Sea States (CBSS). In 2009 the option of
Affiliated Membership was introduced for organisations, institutions and
regions outside the ScanBalt BioRegion. In 2009 ScanBalt published the
Innovation Agenda “Smart Growth: Bridging Academia and SME’s in the Baltic Sea
Region” proposing an EU Baltic Sea Region strategy flagship project ScanBalt
Health Region which was officially approved the same year. In 2012 ScanBalt was
responsible for developing and promoting 'Submariner – Sustainable uses of
Baltic Marine resources' to a new flagship in the EU Baltic Sea Region
strategy. ScanBalt acts as a mediating, coordinating and communicating umbrella
and platform for the Baltic and Nordic regions and the regional networks.
ScanBalt attracts or helps its members attract funding to promote coordinated
private-public cross-border project activities. These focus mainly on creating
regional cross-border infrastructure or to develop private-public cross-border
collaboration within specific thematic areas. Up to 2012 ScanBalt has attracted
or helped to attract approximately EUR 20 M for the members in project funding.
ScanBalt has been involved in many EU-funded projects, including ScanBalt
Competence Region (EU FP 6), Boosting Baltic FP 6 (EU FP 6), Boost Biosystems
(EU FP 6), Trayss Prime (EU FP 6), ScanBalt IPKN (EU FP 6), ScanBalt Campus
(InterregIIIB), Bridge-BSR (EU FP 7 – Coordinator), BSHR HealthPort (Interreg
IV – Coordinator), Eco4Life (South Baltic Programme), ScanBalt Health Region 
(EU BSR Flagship – Coordinator)

Box 5.2 Example of a
regional network focused on a broad, emerging cluster: The Romagna Creative
District, Italy

The
Romagna Creative District (RCD; http://romagnacreativedistrict.com/)
aims to connect and share the creative resources of individuals and companies
to spark off creativity and boost the economy of the region. RCD is active in
the Romagna region in Italy. The network covers twelve creative sectors as
identified by the European Union, including communications, art, design,
architecture, theatre, music and photography.

RCD
has about 1200 members. Standard membership is free, but RCD is planning to
create a sort of premium membership including access advantages and special
services; the fee will probably be different for companies and individuals. RCD
operates as an open platform where new members can always come and participate.
The board consists of 6 members who at the moment, and until the next renewal,
are the 6 founders of the RCD Association. The current president and
vice-president of the Association also participate.

The
RCD secretariat has two full-time and two part-time employees. The cumulative
budget over the last four years has been close to EUR 450 000, i.e. about
EUR 125 000 annually. Roughly 45 % of the necessary funds have been
provided by private companies, 35 % by an EU-funded regional project, 10 %
by foundations, and the remainder by the Chamber of Commerce and a local
municipality.

The
idea for RCD was developed in 2008 and the first formal event to launch the
network took place in May 2009. Barbara Longiardi from Matite Giovanotte, a
design and communication studio based in Forlì, played a central role in
initiating the endeavour. RCD aims to foster creative networking and advertise
the region’s inherent talent and its local assets. The network organises events
to foster networking, such as Ortofabbrica. It also organises international
missions, such as a mission to China in May 2011 where 3 companies from RCD
networks represented Italy at the Shenzen Festival of Creative Industries, and
a joint presence at international conferences such as the 2011 London Design Festival. RCD is
currently not involved in any EU-funded projects.

Box 5.3 An example of
a regional network focused on a cross-cutting theme: Environment Network South
(Miljønetværk Syd), Denmark

The
Environment Network South (ENS -  http://www.milsyd.dk/)
aims to establish and support cooperation between public authorities and
companies in the environmental field, increase knowledge of the environment,
and promote sustainable environmental development for the benefit of citizens
and businesses in the region. The ENS covers the former Ribe County in Denmark,
which includes the municipalities of Fanø, Billund, Varde, Vejen and Esbjerg.
It is open to all industries; the focus is on the environmental impact of the
network members from a variety of industries.

The
ENS has a total of 152 members, 76 of whom are V-members (businesses), 56
I-members (interested parties), 13 F-members (stores endorsing the Green Shop
concept) and 7 O-members (public authorities). Members pay an annual fee
depending on the type of membership. In 2011 Companies (V-members) pay DKK 4 300
per annum if they have less than 50 employees and DKK 6 000 per annum if
they have 50 or more employees. V‑members have the right to vote at the
general meeting and they receive support in preparing their environmental
reviews. Interested parties (I-members) pay DKK 4 300 per annum. They have
the right to speak at the general meeting and they receive newsletters and
invitations to events that are open to network members. Stores (F-members) pay
a registration fee of up to DKK 3 000, depending on the municipality they
are located in, and an annual fee of DKK 500. They may speak at the
general meeting, and they receive the network’s newsletter and the
environmental diploma (the Green Shop concept). Public authorities (O-members)
pay DKK 3 per inhabitant in corresponding municipalities and they have the
right to vote at the network’s annual general meeting.

The
ENS secretariat employs three regular staff, one trainee and two student
workers. Of the three employees in the secretariat, two are working full-time
(37 hours/week) and the third is working only part-time (7 hours/week). The
general assembly is the network's highest authority; it takes place every
spring and all members have the right to attend and speak. The Board consists
of 10 members: 4 members are chosen from among the enterprises undertaking to
prepare an environmental statement which at minimum fulfils the network’s
requirements (the Chairman also comes from among these 4 representatives), 5
mayors or committee chairmen from the public authorities and a representative
of the Environmental Centre of Odense. The ENS has an annual budget of about
DKK 1.8 million, covered largely by membership fees. For special events the ENS
seeks project funding. For the moment the ENS does not have any source of
funding apart from membership fees. However, 2 applications for funding along
with partners are currently in progress. Additionally, for the last 4 years the
network has had a joint programme with other environmental networks in the
region. The ENS does not receive any EU funding at present, but it has
previously participated in 2 projects, one of which ended in 2009 and another
in 2011. The network also has several applications for further funding
currently in progress.

The
ENS was founded in June 1998 by a group of companies in the former Ribe county.
Over the last 14 years, the profile of activities has remained more or less the
same. The Network’s activities aim to have individual members undertake their
own environmental management tasks and attain tangible goals in the
environmental sphere. The network offers practical support to ensure an
overview of the company and provide guidance to the company in its
environmental work. The ENS’s environmental diploma is awarded for a two-year
period and the diploma is renewed when a new environmental statement has been
prepared. In addition, the network organises theme days, lectures and seminars
on environmental topics and gives an annual Environmental Award to a company in
the network that has shown extraordinary commitment to the environment. The
network organises groups where members meet 4-5 times per year to talk about
specified topics. Over time the ENS has increased its focus on education; it
now offers a number of one-day courses on environmental topics. For the time
being the ENS is not participating in any EU-funded projects but has taken part
in one project in the past.

Box
5.4 Case-study of a European network with a topical focus:
Social Firms Europe CEFEC

Social Firms Europe
CEFEC (http://socialfirmseurope.org/) aims to
create paid work for disabled and disadvantaged people and help individuals who
face discrimination to overcome their social and economic exclusion through
employment. Social Firms Network CEFEC wishes to raise  awareness and enhance
the profile of social firms and social cooperatives across Europe, to increase
and serve the membership and to become more financially sustainable and
influential as a European Network. CEFEC is active across Europe and
organisations from outside Europe may also join. Recently the network has taken
in an increasing number of members from Eastern Europe (such as Hungary,
Romania). CEFEC is open to all industries that could help people with
disabilities or disadvantages to find employment.

CEFEC has 43 members and
its annual conference attracts around 150-200 participants. There are 3 types
of members: full members (EUR 150 per year for organisations employing less
than 20 people and EUR 300 per year for organisations with 20 or more
employees); supporting organisation members (EUR 150 per year regardless of
size); and individual members (EUR 25 per year). The secretariat has one
employee, working 20 %. The network is run by an Executive
Committee, responsible for managing the association. It consists of member
representatives, with a minimum of 3 members and a maximum equal to the number
of countries represented in the network. Each member has to be from a different
country. Currently, the Executive Committee has 15 members, including a
treasurer, a secretary and a chairperson. A General Assembly brings together
all the network’s members and supporters, although only full and individual members
have the right to vote. The Assembly decides on the following issues: changing
the articles, appointing and letting go of members of the Executive Committee,
dissolving the association and excluding members. CEFEC has an annual budget of
approximately EUR 10 000. The bulk of the funding (EUR 8 500) comes
from membership fees. About EUR 1 000 comes from projects, and around EUR
1 000 from conference donations. CEFEC has not used EU funding directly
and nor is not planning to do so in the near term. However, they have had
partnerships with other organisations that use EU funding for joint projects.

CEFEC was founded in
1987 by Mr Patrick Daunt, who was in charge of the EU office of Handicapped
Affairs at the time. Initially the network focused on the mentally
handicapped, but in 1989 the Social Firms' movement was widened in scope to
include all disadvantaged people. In 1990 CEFEC became a legal body. In
2007 CEFEC issued the first LINZ-document, the ‘LINZ APPEAL’ which gives
recommendations on Social Firms to the European Union and presents CEFEC’s
research in the area. The network collects data and evidence about the impact
of Social Firms, facilitates networking and sharing of best practice among
members, shares the skills and expertise of its members and encourages and
explores opportunities for further research into the Social Firm model as it
operates in various EU countries. Furthermore, where possible the network
facilitates inter-trading opportunities between Social Firm businesses,
organises annual conferences for its members and hands out the European Social
Firm of the Year Award. The aims and activities are achieved mainly through
annual conferences, but CEFEC’s representatives have also attended other
conferences to introduce the Social Firm model. So far CEFEC has not had direct
participation because the network is very small and not very robust
financially, as the majority of its income comes from membership fees. Although
they cannot have EU-funded projects directly they partner with other organisations
that can. For example, last year CEFEC partnered with ENSIE on their Progress
Project,(funded by the EU) and hopes to continue the cooperation this year.

6. Competitiveness developments along the external borders of
the European Union

Since
the end of the Cold War, most countries sharing a border with the EU have gone
through change on an unprecedented scale. In many ways the European Union has
been an important factor behind this change: successive waves of EU enlargement
have extended its external borders outwards from the borders of the founding
Member States, turning former neighbours into current Member States while
creating new neighbours along its new external borders. Enlargement has had an
impact on the regional economy mainly via improved rule-of-law and business
environment, new trade opportunities, foreign direct investment, cross-border
purchases, commuter and migration flows, and through the acceleration of
structural change (Smallbone et al. 2007). Moreover, the EU has acted as a driver
of change outside its external borders by virtue of its economic and commercial
importance for neighbouring states, as well as its insistence on respect for
democratic principles and human rights.

Table
6.1 illustrates some of the changes over time, starting at a time when the EEC
consisted of its six founding Member States, the combined population of which
was around 200 million. Those six countries were surrounded by 15
countries with a combined population of some 170 million and a combined
GDP of more than half the GDP of the EEC. Since then the number of Member
States has more than quadrupled, the EU population has risen to half a billion
citizens, and many of the 15 countries that surrounded the EEC in 1970 have
themselves become Member States. With the expansion of its external borders at
each stage of enlargement, the EU has gradually gained new neighbours and the
number of countries surrounding the EU has increased from 15 to more than 20.
In parallel with the increasing number of surrounding countries, their combined
population has more than doubled, from 200 million in 1970 to
435 million today. In terms of output, however, the combined GDP of the
countries surrounding the EU today is just a fraction of the latter’s GDP. This
is a reflection not only of the economic success of the EU, but mainly the fact
that many of the countries surrounding it today are relatively poor and
underdeveloped (whereas many of the countries surrounding it in 1970 were at an
economic level comparable to that of the founding Member States).

Table
6.1. Member States and neighbouring states 1970–2010

Year || 1970 || 1980 || 1990 || 2000 || 2010

Number of Member States || 6 || 9 || 12 || 15 || 27

Number of neighbouring states || 15 || 17 || 17 || 24 || 23

Member States’ population in relation to population of neighbouring states || 20% higher || 70% higher || 50% higher || 15% lower || 15% higher

Member States’ total GDP in relation to total GDP of neighbouring states || 60% higher || 150% higher || 330% higher || 180% higher || 340% higher

Source:
Own calculations. Percentages are approximations.

The
focus of this chapter is on the current and future economic and competitiveness
situation in the countries surrounding the EU, with an eye to future-oriented
implications. The following aspects will be specifically addressed:

·
Description of the economic situation and
competitiveness around the external borders of the EU.

·
Existing agreements with the EU or with Member
States; economic impact in terms of foreign direct investment (FDI) and trade
of the agreements.

·
Migration and remittances across the external
borders of the EU; economic impact and impact on competitiveness.

On the
basis of the analysis, conclusions will be drawn and policy implications
formulated covering the challenges and opportunities arising for EU
entrepreneurs and companies operating, or wishing to operate, on the other side
of the external border.

              6.1. The Rim

The
countries covered in this chapter are (shorthand names in brackets, used in the
remainder of the chapter): Republic of Albania (Albania); People’s
Democratic Republic of Algeria (Algeria); Republic of Armenia (Armenia);
Republic of Azerbaijan (Azerbaijan); Bosnia and Herzegovina (BiH);
Arab Republic of Egypt (Egypt); Georgia; State of Israel (Israel);
Hashemite Kingdom of Jordan (Jordan); Kosovo under UN Security Council
Resolution 1244 (Kosovo)[56];
Lebanese Republic (Lebanon); Libya; Principality of Liechtenstein
(Liechtenstein); Republic of Moldova (Moldova); Kingdom of
Morocco (Morocco); Kingdom of Norway (Norway); Occupied
Palestinian Territory (Palestine); Russian Federation (Russia);
Republic of Serbia (Serbia); Swiss Confederation (Switzerland);
Syrian Arab Republic (Syria); Republic of Tunisia (Tunisia); and Ukraine.[57]

In this
chapter, these countries are referred to collectively as ‘the Rim’ – a concept
borrowed from the European Rim Policy and Investment Council (ERPIC) but used
here in a slightly different meaning. Within the Rim, the following four broad
groupings of countries with similar characteristics can be identified:

·
Advanced:
Norway, Switzerland, Liechtenstein, Israel.

·
Eastern Rim:
Armenia, Azerbaijan, Georgia, Moldova, Russia, Ukraine.

·
Western Balkans:
Albania, BiH, Kosovo, Serbia.

·
Southern Rim:
Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Palestine, Syria, Tunisia.

The
countries in the Advanced group are affluent, highly developed and competitive
democracies. Through commercial links as well as agreements and programmes such
as the European Economic Area (EEA)[58],
the Schengen Agreement, and the Framework Programme for Research and
Technological Development, these countries are linked to the EU and some can be
considered Member States in all but name and institutions.

The
Eastern Rim countries are all former Soviet republics and share the
corresponding post-communist legacy. More than 20 years after gaining
independence, most of them are still politically unstable and suffer from
democratic deficits (to varying degrees). The majority of them are low-income
to medium-income economies with a strong adverse legacy in their economic
structures. Despite their relatively low per capita income level, they are
highly industrialised and have an educated population and a relatively well-qualified
labour force. Most Eastern Rim countries also have close ties with the EU in
terms of culture, history and values. Russia (the EU’s strategic partner) does
not aspire to EU membership but is leading alternative integration processes in
the region which, if based on WTO rules, could be compatible with and
complementary to the work of the EU in the region, but which also give rise to
speculation about geopolitical motives. Parts of the Eastern Rim are
potentially competitive, in particular in selected high-technology niche
sectors (related to space and military technology; metals, chemicals and food
industries; tourism) and many of them are important for the supply and transit
of energy to the EU. The negotiation of Deep and Comprehensive Free Trade Areas
(DCFTAs) as part of (also currently negotiated) Association Agreements, has
either started (Armenia, Georgia, Moldova) or has been completed but not signed
for political reasons (Ukraine). Russian is a widely understood language in the
Eastern Rim, an important asset for entrepreneurship and a factor facilitating
regional integration. On the other hand, several ‘frozen conflicts’ (Armenia/Azerbaijan
over Nagorno-Karabakh; Georgia over South Ossetia and Abkhazia, Moldova/Transnistria)
remain unresolved and represent serious obstacles to deeper economic
integration in the region.

The
Western Balkans share many of the characteristics of the Eastern Rim, but are
already candidate countries or potential candidates for EU membership and
therefore institutionally closer to the EU than the Eastern Rim. The region is
fragmented and plagued by serious labour market problems (extremely high
unemployment, migration). Despite persisting tensions and unresolved conflicts,
the shared past has left a lasting positive legacy in the form of negligible
language barriers (except for Albania and Kosovo). There is also a lasting
commercial legacy in the form of the Central European Free Trade Agreement
(CEFTA).

The
Southern Rim economies enjoyed strong economic growth in the 1990s and early
2000s, following a series of economic reforms. Impressive though the reforms
were, they proved unbalanced and unsustainable, giving rise to tensions and
regional imbalances within countries that contributed to their current
instability. The whole region is now in transition and has witnessed
revolutions and outbreaks of violence (in Egypt, Tunisia, Libya, Syria, Palestine
and Lebanon). Democratic processes, free and fair elections, and viable civil
societies are key to sustainable and inclusive growth in the region and are
welcomed by the EU. In the short term though, doing business remains a
challenge in the Southern Rim and EU investment dropped sharply in 2011. The
start of DCFTA negotiations with Egypt, Tunisia, Morocco and Jordan was
approved by the Council in December 2011, marking a step forward in relations
between the EU and those four countries as well as within the Agadir Agreement
Free Trade Zone; the intraregional trade in the Southern Rim is among the
smallest in the world.[59]
Because of their demographic features, the majority of countries in the region
face serious labour market challenges, even if official unemployment is lower
than in the Western Balkans.

              6.2. Economic situation and competitiveness of the Rim countries

Apart
from Switzerland and Norway, the Rim is dominated by three large economies:
Russia and Ukraine on the Eastern Rim; and Egypt in the South. The economic
size of the Rim would be much smaller without these three big countries, which
together account for more than half of the Rim’s population and about half its
GDP. In terms of the structure of the Rim economies, it is only in some energy-exporting
countries – Algeria, Azerbaijan and Libya – that industry gross value added
accounts for more than 50 % of GDP.[60] Elsewhere, the
majority of Rim countries are service-based economies (the share of services is
very high in Albania, Armenia, Georgia, BiH, Moldova, Morocco and Syria), in
many cases also with a relatively large agricultural sector.

In
terms of their share of goods exports in relation to GDP, most Rim countries
are not very open economies and, from that point of view, not very competitive.
In the Southern Rim the lack of openness is clearly linked to the political obstacles
to trade with neighbours in the region (closed frontiers between Morocco and
Algeria, for instance). Several Rim countries specialise in services exports,
the share of which in relation to GDP is higher than for the EU. Services
exports from Rim countries are a mix of transport, tourism and financial
services. Financial services are important in Lebanon and Switzerland, while
tourism plays a decisive role in a number of Southern Rim countries (Egypt,
Morocco and Tunisia). Transport services are fairly important in Georgia and
Ukraine (mainly oil and gas pipelines).

Historically,
more rapid GDP growth or industrial growth has not necessarily been associated
with high export openness. In a number of Rim countries, especially in the
East, relatively rapid GDP or industrial growth from 2000 to 2010 occurred
without particularly high openness. In contrast to most 2004/2007 accession
states and other emerging economies, any economic catching-up in Rim countries
has been the result not of export-led growth but of expanding domestic demand,
frequently financed from remittances or other transfers (Armenia, Georgia and Kosovo).
In the Southern Rim, already existing regional imbalances and exclusion have
been exacerbated by the economic impact of free or special export zones. This
has contributed to the recent revolutions.

Another
common feature is the fairly high external imbalance of many Rim countries. Energy
exporters (Azerbaijan, Russia, Algeria, Libya and Norway) run considerable
trade and current account surpluses – close to 30 % of GDP in the case of
Azerbaijan – whereas the majority of resource-poor Rim countries report high or
even very high (and unsustainable) external deficits (Armenia, Georgia,
Albania, Kosovo, Lebanon and Palestine). Countries that fail to build up a
viable export sector are particularly vulnerable to the kind of effects felt
during the current economic crisis and have to adjust their economic policies accordingly
(Gligorov et al. 2012).[61]

Table 6.2.
Rim countries: overview of economic fundamentals, 2010

EU || 12k || 12k || 100 || 100 || 143 || 116 || 103 || 16.8 || 1.5 || 81.7 || 501 || n/a || n/a || 9.7 || 80.2 || 100 || 100 || 30.4 || 30.9 || 9.7 || 8.4 || –0.2 || 65.0 || 61.9 || || || n/a || n/a || 10k || || PPP: purchasing power parity. LFS: labour force survey. S: stability and association agreement. F: free trade agreement. E: Eastern partnership. P: partnership and cooperation agreement. eea: European economic area. efta: European free trade association. k = thousand || Sources: Eurostat, national statistics, AMECO, IMF, UNCTAD, UN Comtrade, OECD, World Bank, Coface, European Commission and High Representative (2012c).

Ukr || 104 || 249 || 2.03 || 22 || 65.8 || 152 || 155 || 24.4 || 7.2 || 68.4 || 45.9 || 88.4 || 93.3 || 8.1 || 39.5 || 42 || 7.7 || 37.8 || 44.2 || 12.4 || 8.8 || –2.1 || 25.4 || 31.4 || 0.45 || 0.29 || 152 || E || 954 ||

Tuni || 33.4 || 76.9 || 0.63 || 30 || 245 || 155 || 123 || 30.0 || 7.8 || 62.3 || 10.5 || 129 || 110 || 13.0 || 43.5 || 43 || n/a || 37.1 || 47.4 || 13.1 || 7.6 || –4.8 || 72.1 || 57.3 || 0.26 || 0.29 || 46 || F || 2285 ||

Syri || 44.7 || 83.1 || 0.68 || 16 || 247 || 155 || 120 || 33.7 || 21.0 || 45.3 || 21.0 || 165 || 127 || 8.4 || 28.5 || 54 || n/a || 20.2 || 25.8 || 8.9 || 5.3 || –3.9 || 35.6 || 25.0 || 0.09 || 0.10 || 134 || F || 272 ||

Swit || 399 || 286 || 2.34 || 146 || 131 || 118 || 118 || 26.8 || 1.2 || 72.0 || 7.79 || 116 || 108 || 4.6 || 20.2 || 139 || 182 || 49.0 || 46.6 || 15.8 || 7.5 || +16 || 58.7 || 77.5 || 2.18 || 2.76 || 26 || efta || 53k ||

Serb || 29 || 62 || 0.51 || 35 || n/a || 150 || 106 || 18.4 || 8.0 || 73.6 || 7.30 || n/a || 97.1 || 19.2 || 36.0 || 47 || 16.6 || 25.5 || 42.0 || 9.2 || 9.2 || –7.2 || 57.3 || 56.0 || 0.19 || 0.10 || 92 || S || 2164 ||

Rus || 1115 || 1808 || 15 || 52 || 107 || 159 || 149 || 26.7 || 3.5 || 69.8 || 143 || 96.6 || 97.5 || 7.5 || 8.6 || 62 || 18.9 || 27.2 || 16.9 || 3.0 || 5.0 || +4.8 || 52.6 || 41.6 || 2.23 || 3.92 || 120 || P || 1750 ||

Pale || 5.57 || n/a || n/a || n/a || n/a || n/a || 107 || 24.3 || 21.6 || 54.1 || 4 || n/a || n/a || 24.0 || n/a || n/a || n/a || 13.1 || 65.4 || n/a || n/a || –8.9 || 2.1 || 8.1 || 0.00 || 0.00 || 131 || F || n/a ||

Nor || 312 || 214 || 1.74 || 179 || 168 || 116 || 85 || 40.1 || 1.2 || 58.7 || 4.89 || 115 || 108 || 3.6 || 49.7 || 146 || 210 || 32.1 || 18.0 || 9.6 || 10.4 || +12 || 80.9 || 63.3 || 2.04 || 1.09 || 6 || eea || 27k ||

Mor || 68.7 || 118 || 0.96 || 15 || 205 || 162 || 137 || 37.3 || 19.9 || 52.8 || 31.9 || 132 || 112 || 9.1 || 26.1 || 58 || n/a || 19.3 || 35.8 || 13.8 || 8.2 || –4.3 || 59.3 || 51.8 || 0.22 || 0.36 || 94 || F || 967 ||

Mol || 4.46 || 8.40 || 0.07 || 10 || 57.2 || 165 || 136 || 13.2 || 11.9 || 74.8 || 3.56 || 92 || 98 || 7.4 || 26.3 || 53 || 7.0 || 35.7 || 85.4 || 15.5 || 17.3 || –12 || 51.9 || 43.4 || 0.04 || 0.02 || 81 || E || 600 ||

Liec || 3.58 || 2.57 || 0.02 || 293 || n/a || n/a || n/a || 36 || 6 || 58 || 0.04 || n/a || n/a || 3.2 || n/a || n/a || 151 || n/a || n/a || n/a || n/a || +25 || 62.4 || 89.0 || n/a || n/a || n/a || eea || n/a ||

Liby || 53.8 || 70.1 || 0.57 || 44 || 149 || 147 || 140 || 78.2 || 1.9 || 19.9 || 6.56 || 150 || 123 || n/a || 2.5 || 77 || n/a || 63.0 || 37.4 || 0.7 || 8.6 || +14 || 75.7 || 48.3 || 0.74 || 0.18 || n/a || F || 2138 ||

Leb || 29.6 || 45.9 || 0.37 || 48 || 331 || 166 || 110 || 17.7 || 4.8 || 77.6 || 3.91 || 138 || 110 || 6.4 || 145 || 64 || n/a || 13.9 || 45.2 || 38.9 || 33.2 || –11 || 15.3 || 36.5 || 0.01 || 0.12 || 104 || F || 6226 ||

Kos || 4.26 || 9.31 || 0.08 || 17 || n/a || 178 || 120 || 20 || 12 || 68 || 2.21 || n/a || n/a || 45 || 6.1 || 46 || n/a || 7.2 || 47.6 || 12.2 || 11.1 || –15 || 44.7 || 38.3 || 0.02 || 0.00 || 117 || – || n/a ||

Jord || 19.9 || 27.2 || 0.22 || 18 || 292 || 184 || 146 || 34.3 || 2.8 || 62.9 || 6.11 || 176 || 126 || 12.5 || 67 || 73 || n/a || 26.6 || 51.7 || 19.5 || 16.1 || –4.9 || 4.2 || 20.9 || 0.01 || 0.07 || 95 || F || 2341 ||

Isr || 164 || 170 || 1.38 || 93 || 238 || 136 || 119 || 27.0 || 3.0 || 70.0 || 7.43 || 165 || 122 || 6.7 || 74.7 || 97 || n/a || 25.6 || 26.7 || 11.4 || 8.3 || +2.9 || 26.0 || 35.0 || 0.31 || 0.38 || 34 || F || 8060 ||

Geo || 8.79 || 17.1 || 0.14 || 16 || 68.8 || 183 || 130 || 12.1 || 7.3 || 80.6 || 4.45 || 81 || 100 || 16.3 || 36.7 || 51 || 9.3 || 21.1 || 43.2 || 13.7 || 9.2 || –9.6 || 18.7 || 28.3 || 0.03 || 0.01 || 16 || E || 1300 ||

Egy || 165 || 385 || 3.14 || 20 || 248 || 162 || 133 || 37.3 || 19.9 || 52.8 || 77.8 || 152 || 123 || 9.0 || 78 || 43 || n/a || 12.2 || 21.2 || 11.4 || 7.4 || –2.0 || 35.5 || 27.1 || 0.19 || 0.39 || 110 || F || 650 ||

BiH || 12.5 || 24.9 || 0.20 || 27 || n/a || 143 || 187 || 17.8 || 7.1 || 75.1 || 3.84 || n/a || 102 || 27.2 || 39.1 || 50 || 22.4 || 29.8 || 55.7 || 7.8 || 3.6 || –5.6 || 54.5 || 45.9 || 0.08 || 0.05 || 125 || S || 1500 ||

Aze || 39.2 || 69.3 || 0.57 || 32 || 237 || 402 || 326 || 52.6 || 5.4 || 42.0 || 9.05 || 124 || 113 || 5.6 || 7.4 || 57 || 11.0 || 51.1 || 13.0 || 4.0 || 7.3 || +29 || 47.6 || 25.3 || 0.06 || 0.25 || 66 || E || 400 ||

Arm || 7.06 || 12.8 || 0.10 || 16 || 146 || 216 || 161 || 14.8 || 17.4 || 67.8 || 3.25 || 90.0 || 101 || 7.0 || 39.4 || 55 || 7.9 || 12.2 || 33.7 || 8.1 || 10.7 || –15 || 49.6 || 23.0 || 0.01 || 0.01 || 55 || E || 1000 ||

Alg || 119 || 194 || 1.58 || 22 || 170 || 145 || 108 || 54.5 || 11.7 || 33.7 || 36.1 || 144 || 119 || 10.0 || 11.1 || 61 || n/a || 32.3 || 26.8 || 2.1 || 8.4 || +7.9 || 52.0 || 52.9 || 0.54 || 0.41 || 148 || F || 364 ||

Alb || 8.85 || 21.7 || 0.18 || 28 || 197 || 171 || 234 || 8.9 || 16.8 || 74.3 || 3.21 || 99.9 || 105 || 15.0 || 61.0 || 41 || 8.9 || 13.2 || 36.8 || 19.2 || 17.2 || –12 || 70.1 || 64.6 || 0.05 || 0.02 || 82 || S || 960 ||

Country || GDP at exch. rates, EUR bn || GDP at PPP, EUR bn || GDP at PPP, EU=100 || GDP at PPP per cap, EU=100 || GDP volume, 1990=100 || GDP volume, 2000=100 || Industrial output, 2000=100 || Share of industry in GDP % || Share of agriculture in GDP || Share of services in GDP % || Population (million) || Population, 1990=100 || Population, 2000=100 || Unemployment rate (LFS) % || Public debt, % of GDP || Price level, EU=100 || Average wages, EU=100 || Exports of goods, % of GDP || Imports of goods, % of GDP || Export of services, % of GDP || Import of services, % of GDP || Current account, % of GDP || Exports to EU, % of exports || Imports fr. EU, % of imports || Share of EU total exports, % || Share of EU total imports, % || Doing Business rank (2012) || Institutional arrangement || FDI stock per capita, EUR ||

In
absolute terms, the Rim countries are relatively minor EU trading partners. Less
than 10 % of total EU exports and less than 11 % of total EU imports
were accounted for by trade with the Rim countries in 2010. At the same time
there is an asymmetry in the relative importance of EU-Rim trade. For most Rim
countries, the EU is by far their most important export and import partner.
This is especially true for the Eastern Rim (with the possible exception of
Georgia). Distinct geographical trading patterns exist at the sub-regional
level as well. Conversely, the competitiveness and trade balances of EU Member
States such as France, Spain, Italy and Greece are significantly affected by
their trade with Rim countries.

This
trade asymmetry has important consequences for the competitiveness of the Rim. Any
EU policy or measure that affects trade relations with the Rim countries, in
particular a free trade agreement, has a disproportionately large impact on the
latter countries. This also applies to individual EU Member States if they
maintain particularly close trading links with certain Rim countries (cases in
point include Poland and Ukraine, France and Tunisia, Spain and Morocco, and Romania
and Moldova) or are trading in a particular sector.

Similarly,
from an EU point of view the assessment of the competitiveness of Rim economies
depends on the political situation, their investment climate and other
conditions for doing business. Here again, the Rim countries differ widely (cf.
Figure 6.1). Several Rim countries have improved the conditions for doing
business in recent years, notably Morocco, Moldova and Armenia. According to
the World Bank (2011a), SMEs that benefit most from these improvements are the
key engines for job creation. In this context it is useful to note that SMEs
employ 25 % of the active work force in the Southern Mediterranean
(European Parliament 2012).

Financial
intermediation is generally underdeveloped in Rim countries, as demonstrated,
for instance, by the relatively low percentage of firms that operate with a
bank loan or a credit line. Lending practices thus pose a serious obstacle; a
fact of particular relevance to the development of SMEs (Alvarez
de la Campa 2011). The practices of the informal economy (crime
and corruption) are frequently mentioned as important obstacles, especially in
Eastern Rim countries. The Southern Rim has also long been faced with certain corrupt
practices, for instance when obtaining an import licence, a construction
permit, a mains electricity connection, or a government contract. It is too
early to tell whether this will change in the wake of the Arab Spring and
subsequent elections. Whereas only a small proportion of Rim firms possess an
internationally recognised quality certificate, a relatively high proportion of
firms use internet (slightly more in the East than in the South). By contrast,
only a small percentage of firms use technologies licensed from abroad (again,
more firms in the East than in the South).

Figure
6.1. Main obstacles to doing business (2009), shares (%) of firms surveyed

Source:
Enterprise Surveys, World Bank

In
addition to overall rankings, the World Bank Enterprise Surveys provide a
number of additional results which are relevant for assessing the business
environment and competitiveness, particularly of SMEs. These indicators assess
several areas with an impact on entrepreneurship and firm competitiveness (such
as regulations and taxes, access to finance, corruption, crime, infrastructure,
various characteristics of firms and labour, innovation and technology). In
each country covered by the survey, several hundred firms – usually
domestically-owned SMEs operating in the non-agricultural, formal, private
economy – are surveyed. Figure 6.1 illustrates the eight most important obstacles
to doing business in the Rim, as identified by respondents (usually the owners
or managers of SMEs) in the individual Rim countries. These eight obstacles
account for 60 % to 70 % of all obstacles surveyed in most Rim
countries covered (except for Jordan, Lebanon, Ukraine and Palestine, where
other obstacles were more important). The Euro-Mediterranean Charter for
Enterprise was adopted by ministers in 2004 to address some of the obstacles.
Inspired by EU policies to promote SMEs, it includes guidelines for spurring
entrepreneurship and improving the business climate. Since its adoption, it has
been a key document for guiding reforms in Mediterranean neighbouring
countries. It has also been used as a platform for exchanging good practice
across the Euro-Mediterranean area.

Labour
regulations are not perceived as a major constraint by the majority of firms,
especially in the more market-oriented and liberal Eastern Rim. An inadequately
educated workforce is seen as a constraint by a substantial percentage of firms
in the Southern Rim, in particular in Algeria, Egypt, Lebanon and Syria. In
Eastern Rim countries, lack of education is perceived to be much less of a
constraint: firms in those countries also employ fewer unskilled workers and –
crucially important for competitiveness – a higher proportion of Eastern Rim
firms offer their workers formal training (46 % of firms in Armenia, and about
50 % in BiH, Moldova, Russia and Ukraine). The fairly high level of
qualification of the labour force also represents one of the key competitive
advantages of Eastern Rim firms, despite a decline in the quality of education
since the fall of the Soviet Union (OECD 2011).

              6.3. Trade relations between the EU and the Rim

Most
Rim economies are small and, with the exception of Russia, Norway, Switzerland
and Israel, play a limited role in global trade. With the exception of Russia
and Switzerland, none of these countries account for more than 1 % of
world import demand.

Grouping
the Rim countries regionally, the Southern Rim and the Western Balkans each
account for no more than 1.2 % to 1.5 % of global exports (WTO 2011).
Were it not for the exports of Russia, the figure for the Eastern Rim would be
of a similar magnitude.

Notwithstanding
considerable liberalisation efforts in Eastern Rim and Southern Rim countries, overall
Rim countries do not have successfully implemented the kind of extensive and
export-led growth strategy that would diversify and upgrade their export base
and integrate their economies into global trade networks. In terms of exports
by broad economic sector, manufacturing is the least developed in Russia (where
manufacturing accounts for 18 % of total exports) and the Southern Rim.
Switzerland is at the opposite end, as its export structure is geared towards
manufactured goods (63 % of total exports). Algeria, Libya, Azerbaijan and
Russia, which depend mainly on commodity exports, are caught in a type of
resource trap, where rents from natural resources turn out to be detrimental to
export diversification and structural upgrading. The share of manufactured
goods in total exports is also below the global average in Norway, due to its
high share of energy exports.

Turning
to services, in many countries the bulk of export revenues comes from
‘traditional’ service sectors such as travel (tourism) and, to a lesser extent,
logistics and transport services. A disproportionately high share of services
in overall exports can be observed in Albania, Armenia, Georgia, Lebanon,
Egypt, Morocco and Tunisia. The lack of any significant manufacturing export
base makes tourism (travel services) the single most valuable export item in
resource-scarce, less-developed countries. Most of the resource-poor Rim
countries – which should be more inclined to develop manufacturing capacities
because they cannot rely on rents from natural resources – have not managed to
diversify their exports enough and move into manufacturing (see Masood 2010;
Eurochambres 2011, López-Cálix et al. 2010).

Figure
6.2. Export structure of Rim countries by broad sector (2010), shares (%)

Note: Commodity exports are calculated as
merchandise exports less manufacturing exports. Data for Kosovo, Liechtenstein
and Palestine are not available. For Syria and Libya, data refer to 2009.

Source:
WTO database; background study.

As a
consequence of the lack of an export manufacturing base some Rim countries,
particularly in the South and the East, are forced to compete mainly on price
in areas with static comparative advantages from natural resource endowments.
Hence, their competitiveness in international markets remains based on the
abundance of resources and, with the possible exceptions of Tunisia and Morocco,
these countries are still in transition from ‘factor-driven’ to
‘efficiency-driven’ economies (Porter et al. 2002). While in developed
economies such as the EU, Norway, Switzerland, Liechtenstein and Israel,
innovation and technological leadership in products and services are key to
success in international markets (cf. European Commission (2010c) for a
discussion of Swiss and EU competitiveness in key enabling technologies), such
factors are so little developed in most Rim countries that they offer no basis
for export success. Hence the importance attached to the neighbourhood in the EU
framework programme for RTD, and its support to science, technology and
innovation through ENP programmes.

On
aggregate, Rim countries account for some 27 % of extra-EU merchandise
exports and 29 % of extra-EU merchandise imports. Of the 27 % of
extra-EU exports, more than a third (11 %) are exported to EEA/EFTA
countries, followed by Russia (6 %) and North Africa (5 %). The
29 % of extra-EU imports come mainly from EEA/EFTA countries (11 %)
and Russia (also 11 %), the latter largely due to energy imports.

Table 6.3.
EU merchandise exports to Rim countries/groups of Rim countries (2010)

|| Destination region

Exporter || EEA- EFTA || Potential candidate countries || Eastern Partnership countries || Russia || North Africa || Mediterra-nean Middle East (excl. Israel) || Israel || Extra-EU total

EU27 || value, million  € || 148198 || (100%) || 13253 || (100%) || 22936 || (100%) || 86131 || (100%) || 61882 || (100%) || 11236 || (100%) || 14405 || (100%) || 1349610 || (100%)

|| share of  exports || 10.98% || || 0.98% || || 1.70% || || 6.38% || || 4.59% || || 0.83% || || 1.07% || || 100% ||

|| export growth || 4.03% || || 8.97% || || 12.48% || || 14.25% || || 6.68% || || 5.44% || || –1.22% || || 4.74% ||

DE, AT, || value, million € || 70976 || (47.9%) || 3790 || (28.6%) || 8595 || (37.5%) || 38705 || (44.9%) || 15084 || (24.4%) || 3782 || (33.7%) || 6559 || (45.5%) || 596105 || (44.2%)

Benelux || share of  exports || 11.91% || || 0.64% || || 1.44% || || 6.49% || || 2.53% || || 0.63% || || 1.10% || || 100% ||

|| export growth || 4.61% || || 9.40% || || 12.31% || || 14.34% || || 7.33% || || 5.64% || || –1.85% || || 6.25% ||

Northern || value, million € || 20038 || (13.5%) || 158 || (1.2%) || 934 || (4.1%) || 8179 || (9.5%) || 2677 || (4.3%) || 547 || (4.9%) || 636 || (4.4%) || 100352 || (7.4%)

EU || share of  exports || 19.97% || || 0.16% || || 0.93% || || 8.15% || || 2.67% || || 0.54% || || 0.63% || || 100% ||

|| export growth || 3.66% || || –0.48% || || 9.1% || || 9.45% || || 5.09% || || 3.60% || || –1.95% || || 3.34% ||

Western || value, million € || 13918 || (9.4%) || 216 || (1.6%) || 1154 || (5.0%) || 3960 || (4.6%) || 3171 || (5.1%) || 1008 || (9.0%) || 1692 || (11.7%) || 178043 || (13.2%)

EU || share of  exports || 7.82% || || 0.12% || || 0.65% || || 2.22% || || 1.78% || || 0.57% || || 0.95% || || 100% ||

|| export growth || 1.98% || || 7.79% || || 11.78% || || 12.27% || || 2.40% || || 4.51% || || –5.10% || || 1.39% ||

Southern || value, million € || 34884 || (23.5%) || 3759 || (28.4%) || 3304 || (14.4%) || 16639 || (19.3%) || 38151 || (61.7%) || 4961 || (44.2%) || 4190 || (29.1%) || 375763 || (27.8%)

EU || share of  exports || 9.28% || || 1% || || 0.88% || || 4.43% || || 10.15% || || 1.32% || || 1.11% || || 100% ||

|| export growth || 2.68% || || 6.80% || || 9.65% || || 12.27% || || 6.68% || || 5.00% || || –0.31% || || 3.35% ||

Eastern || value, million € || 8382 || (5.7%) || 5330 || (40.2%) || 8949 || (39.0%) || 18649 || (21.7%) || 2800 || (4.5%) || 938 || (8.4%) || 1328 || (9.2%) || 99347 || (7.4%)

EU || share of  exports || 8.44% || || 5.36% || || 9.01% || || 18.77% || || 2.82% || || 0.94% || || 1.34% || || 100% ||

|| export growth || 18.84% || || 11.82% || || 22.68% || || 26.65% || || 16.94% || || 11.75% || || 17.38% || || 17.81% ||

Source:
Eurostat Comext; background study.

Table
6.4. EU merchandise imports to Rim countries/groups of Rim countries (2010)

|| Source region

Importer || EEA- EFTA || Potential candidate countries || Eastern Partnership countries || Russia || North Africa || Mediterra-nean Middle East (excl. Israel) || Israel || Extra-EU total

EU27 || value, million € || 163687 || (100%) || 7152 || (100%) || 22587 || (100%) || 160058 || (100.0) || 74801 || (100%) || 4213 || (100%) || 11087 || (100%) || 1509090 || (100%)

|| share of  imports || 10.85% || || 0.47% || || 1.50 || || 10.61 || || 4.96% || || 0.28% || || 0.73% || || 100% ||

|| import growth || 3.99% || || 13.77% || || 13.37 || || 9.64 || || 5.22% || || 0.46% || || 0.45% || || 4.28% ||

DE, AT, || value, million € || 76196 || (46.5%) || 2038 || (28.5%) || 4411 || (19.5%) || 60028 || (37.5) || 14324 || (19.1%) || 1998 || (47.4%) || 4969 || (44.8%) || 622667 || (41.3%)

Benelux || share of  imports || 12.24% || || 0.33% || || 0.71 || || 9.64 || || 2.3% || || 0.32% || || 0.80% || || 100% ||

|| import growth || 5.73% || || 14.81% || || 8.82 || || 11.11 || || 3.06% || || 0.99% || || –0.56% || || 5% ||

Northern || value, million € || 16467 || (10.1%) || 53 || (0.7%) || 219 || (1.0%) || 15247 || (9.5) || 400 || (0.5%) || 23 || (0.5%) || 232 || (2.1%) || 74488 || (4.9%)

EU || share of  imports || 22.11% || || 0.07% || || 0.29 || || 20.47 || || 0.54% || || 0.03% || || 0.31% || || 100% ||

|| import growth || 2.24% || || 2.00% || || 13.56 || || 12.14 || || 9.46% || || 2.86% || || –3.17% || || 3.86% ||

Western || value, million € || 30688 || (18.7%) || 118 || (1.7%) || 524 || (2.3%) || 5888 || (3.7) || 4327 || (5.8%) || 91 || (2.2%) || 1661 || (15.0%) || 220122 || (14.6%)

EU || share of  imports || 13.94% || || 0.05% || || 0.24 || || 2.67 || || 1.97% || || 0.04% || || 0.75% || || 100% ||

|| import growth || 4.96% || || 12.51% || || 10.41 || || 6.07 || || 4.79% || || –5.66% || || –1.38% || || 0.87% ||

Southern || value, million € || 35056 || (21.4%) || 2586 || (36.2%) || 11016 || (48.8%) || 37630 || (23.5) || 54833 || (73.3%) || 2002 || (47.5%) || 3338 || (30.1%) || 453528 || (30.1%)

EU || share of  imports || 7.73% || || 0.57% || || 2.43 || || 8.30 || || 12.09% || || 0.44% || || 0.74% || || 100% ||

|| import growth || 1.13% || || 9.64% || || 16.59 || || 8.36 || || 5.86% || || 0.03% || || 2.52% || || 4.18% ||

Eastern || value, million € || 5280 || (3.2%) || 2357 || (33.0%) || 6417 || (28.4%) || 41265 || (25.8) || 916 || (1.2%) || 99 || (2.3%) || 887 || (8.0%) || 138288 || (9.2%)

EU || share of  imports || 3.82% || || 1.70% || || 4.64 || || 29.84 || || 0.66% || || 0.07% || || 0.64% || || 100% ||

|| import growth || 9.08% || || 22.82% || || 18.43 || || 14.65 || || 9.95% || || 11.82% || || 10.39% || || 14.39% ||

Source:
Eurostat Comext; background study.

Tables 6.3
and 6.4 show bilateral trade relations between parts of the EU and individual
Rim countries or groups of countries and provide a clear illustration of the
heterogeneity of EU Member States in this respect. It is
clear that the Rim is not necessarily a focus area for core EU Member States
such as Germany, Austria and the Benelux countries. The same is true for
Northern EU, albeit with the qualification that it is clearly overrepresented
in trade with the EEA/EFTA (because of Norway) and strongly under­represented
in trade with Israel. Western EU is underrepresented in exports to all Rim
regions, as its trade is more concentrated on the USA and Japan. By contrast,
parts of the Rim are important export destinations for Southern EU countries
and also for Eastern EU – Southern EU accounts for 62 % of total EU
exports to North Africa. Two obvious reasons for this are their geographical
proximity and colonial heritage. Another clearly discernible pattern is the
export orientation of Eastern EU towards the Eastern Rim, a legacy of previous economic
relations within Central and Eastern Europe. The share of Eastern EU exports to
total EU exports to the potential candidates in the Western Balkans is also
high (40 %), again explained by their geographical proximity and the close
trade relations that used to exist within Yugoslavia and now prevail in the Central
European Free Trade Agreement (CEFTA).

Primary
commodity exports (apart from oil) account for a significant share of exports
to the EU from a number of Rim countries, including Armenia, Georgia and
Ukraine (Table 6.5). Countries such as Tunisia and Morocco, Moldova,
Ukraine, Georgia and the Mediterranean Middle East tend to export a
proportionally higher share of agricultural sector output to the EU. However,
agricultural exports from these countries to the EU are sometimes hampered by
non-conformity with EU legislation on food safety and animal feed (Eurochambres
2011). Turning to manufacturing, bilateral trade relations between the EU and
resource-rich Rim countries mirror the general export structure of the latter,
characterised by a lack of manufactured goods (with the notable exception of
Switzerland and Israel). Rim countries generally have industrial export
capacities in ‘early stages’ manufacturing industries with low technology intensity,
such as agricultural products and textiles. The textile industry, for example,
constitutes 45 % of Albania’s total exports to the EU; the share is
similar for Moldova and somewhat lower, around 34 %, for Morocco and
Tunisia. The food industry is a strong export sector in Serbia (13 % of
total exports) and Lebanon (11 %); it is also important for Ukraine and
Kosovo.

Table 6.5.
EU exports to and imports from EaP countries by product category

|| Exports to EaP countries || Imports from EaP countries

(EUR million) || January-June 2010 || January-June 2011 || January-June 2010 || January-June 2011

Manufactured goods – chemicals – machinery and vehicles – other manufactures || || 10625 2360 4757 3509 || || || 13672 2807 6781 4083 || || || 3784 413 676 2695 || || || 5733 776 842 4114 ||

Primary goods – food and drink – raw materials – energy || || 1983 1058 288 638 || || || 2543 1287 385 871 || || || 7662 285 1525 5852 || || || 11732 720 2025 8988 ||

Other || || 198 || || || 274 || || || 207 || || || 284 ||

Total || || 12807 || || || 16489 || || || 11652 || || || 17749 ||

Source:
Eurostat.

Countries
wishing to build up manufacturing often start by developing their export
capacities in the textile, leather and first processing food industries, as
these sectors depend more on cheap labour than on technology. However,
increasingly globalised supply chains and greater opportunities for
multinational firms to relocate production processes to other countries have
made it possible for countries to attract the foreign direct investment
associated with such offshoring activities and move straight into more
technology-intensive industries. This has happened, for example, in some
2004/2007 accession states now integrated in the European automotive industry
network. Outside Europe it has taken place in China, Malaysia and Thailand,
which have become part of the Asian electronics cluster originally formed
around Japan and South Korea. However, in the current economic climate such
developments can be observed only on a small scale and in a small group of Rim
countries such as Serbia and BiH among the Western Balkan countries, and
Tunisia and Morocco in the South.

While
imports from the Rim countries tend to be concentrated to certain goods, mainly
primary commodities, EU exports to the Rim are well diversified and reflect the
overall export structure of the EU, with a focus on manufactured goods related
to transport equipment, chemicals and machinery, as well as electronics. Taking
the revealed comparative advantages (RCAs) of the trade of
the EU as a proxy for sectoral competitiveness, the EU has a pronounced
comparative disadvantage in primary industries, including agriculture, fishing,
mining and quarrying (cf. Figure 6.3). By contrast, the EU has a strong
revealed comparative advantage in high-technology and medium-high-technology
industries such as chemicals (except pharmaceuticals), machinery and
automotives. Its revealed comparative disadvantage in low-technology industries
is mainly due to the fact that several Rim countries (Albania, BiH, Moldova,
Morocco, Tunisia and Egypt) have substantial textile industries. In the medium-low-technology
industries, the metals and mineral industries explain the positive RCAs of
Armenia and Ukraine. In the case of Russia, it is mainly the petroleum-refining
industry that explains the revealed comparative disadvantage of the EU. As
regards the EEA/EFTA countries as well as Israel, the EU is in almost the
opposite position – at least in its trade with Switzerland, Liechtenstein and
Israel – since it has positive RCAs in low-technology and medium-low-technology
industries, but a comparative disadvantage in high-technology industries.

Figure 6.3. Revealed comparative
advantages (RCAs) in EU trade with the Rim; industries classified by technology
content (2010)

Note: Industry groupings according to OECD technology
classification (OECD 2003).

Source:
Eurostat Comext; background study.

Box 6.1. Effects of EU
trade liberalisation

Almost all Rim countries have signed free
trade agreements (FTAs) with the EU; where such agreements do not exist there
tend to be EU autonomous trade measures (ATMs) or a generalised system of
preferences (GSP) in their place. As a consequence, the average EU tariff rate
vis-à-vis the Rim was no more than 1.4 % in 2010. By contrast, EU
exporters face an average weighted tariff rate of 5 % when exporting to
the Rim countries, with some rates reaching as high as 19 %. As a core
component of the Europe 2020 strategy for growth, EU trade policy pursues ‘deep
and comprehensive FTAs’ (DCFTAs) as part of future Association Agreements
within the framework of the Eastern Partnership and the Euro-Mediterranean
Partnership. The aim is to bring all its neighbours gradually closer to the
single market through regulatory convergence. As a result, the average tariff
faced by EU exports of industrial products is expected to fall from 5 % to
about 1.7 %. The combined growth effects of its different FTAs would be to
add up to 1.5 % to EU GDP in the long term (European Commission 2010a;
European Commission 2011 b).

              6.4. Foreign direct investment effects

Foreign
direct investment (FDI) – discussed in a previous chapter of this report –
illustrates the intensity at firm level of integration between countries. The
ability to attract inward FDI flows confirms the competitiveness of a host
country location for production and services. The intensity of outward FDI flows,
on the other hand, indicates the competitiveness of home country multinational
corporations (MNCs) in capturing foreign markets. Companies expand abroad either
to capture new markets (horizontal or market-seeking FDI) or in order to
optimise their production by allocating stages of production to the most
efficient location (vertical or efficiency-seeking FDI). Both types of FDI have
important growth effects at firm level by increasing production, expanding into
new markets and reducing production costs. FDI also has productivity effects as
a result of economies of scale and lower production costs. In addition, FDI may
provide access to scarce natural, human and R&D resources (resource-seeking
FDI). Globally, outsourcing activity has declined during the current crisis,
and in future ‘near-shoring’ may be preferred
to ‘far-shoring’ FDI. This provides an
opportunity for the Rim countries to benefit from EU offshoring. The aims of
analysing the size of FDI flows between the EU and the Rim countries are to determine
the existing intensity of direct investment links, explore the impact of these
links on the competitive position of Member States, and look for location
advantages in the region that could be exploited by EU firms in years to come.

In
recent years, the EU has intensified its FDI exchanges with countries outside
the EU. Inward FDI flows from the Rim have fluctuated around their average of
EUR 16.9 billion over the last ten years (24.4 % of total
extra-EU inward flows). In 2007, inward FDI from the Rim peaked at
EUR 38.4 billion, followed however by almost no inward flow in the
subsequent year. In 2010, firms in Rim countries invested
EUR 14.5 billion in the EU. The last three years point to
lower-than-average inward flows from the Rim, indicating a possible loss of
competitiveness of this region on EU markets.

In
terms of outward FDI flows from the EU, the share of the Rim was 42 %
(EUR 84.6 billion) in 2009 and 28 % (EUR 55.2 billion)
of total extra-EU FDI in 2010, far above the ten-year average of 17 %. The
Rim countries have thus benefited from the shift of FDI to extra-EU countries
(cf. Chapter 4.3). Among the Rim countries, Norway and in particular
Switzerland naturally account for the bulk of outward FDI from the EU to the Rim
and of inward FDI to the EU from the Rim. Inward FDI flows from the rest of the
Rim are on a much smaller scale and have been characterised by divestment in
2008–2010 (Figure 6.4), whereas the same countries have received
significant FDI flows from the EU (Figure 6.5). Particularly large outward
flows from the EU to the region were recorded in the run-up to the current
economic crisis. This reflects the global trend towards a peak in international
FDI in 2008, followed by much smaller FDI flows subsequently, as a result of
the crisis.

Figure
6.4. Inward FDI flows to the EU from the Rim (excl. EEA/EFTA), EUR million

Note: EU is EU25 for
2001–2003, EU27 for 2004–2010. EU flows calculated as the sum of flows to
Member States. Intra-EU flows to Luxembourg are adjusted downwards by 90 %
in order to exclude activities of special purpose enterprises (SPEs). Extra-EU
flows exclude offshore centres (Guernsey, Jersey, Isle of Man, Gibraltar,
Bahamas, Bermuda, British Virgin Islands, Cayman Islands, Netherlands
Antilles).

Source:
Eurostat; background study.

A
closer look at inward FDI to the EU from non-EFTA Rim countries reveals Russia
to be the main investor. Russian firms accounted for most inward non-EFTA FDI
in 2006 and 2007 (Figure 6.4) but were also responsible for the massive capital
withdrawals afterwards.

Until
2008, Russia was also the prime destination for outward non-EFTA FDI, often
with more than half of total non-EFTA flows (Figure 6.5). As a result, EU
companies account for an overwhelming share (83 %) of the total FDI stock
in Russia. It should however be noted that no less than a third of the EU stock
of FDI in Russia is owned by Cypriot firms, making Cyprus the largest investor
country in Russia. The large Cypriot stock is mainly the result of flows of
Russian capital being channelled through Cyprus for tax purposes, so-called
round-tripping (Hunya and Stöllinger 2009). Proper EU investments in the Russian
real economy may therefore be overstated by as much as a third.

Figure
6.5. Outward FDI flows from the EU to the Rim (excl. EEA/EFTA), EUR million

Note: EU is
EU25 for 2001–2003, EU27 for 2004–2010. EU
flows calculated as the sum of flows to Member States. Intra-EU flows to
Luxembourg are adjusted downwards by 90 % in order to exclude activities
of special purpose enterprises (SPEs). Extra-EU flows exclude offshore centres
(Guernsey, Jersey, Isle of Man, Gibraltar, Bahamas, Bermuda, British Virgin
Islands, Cayman Islands, Netherlands Antilles).

Source:
Eurostat; background study.

Another
important destination for EU investments in the non-EFTA part of the Rim is the
Southern Rim, in particular Egypt and Morocco. Over the last ten years, both
countries have received about EUR 1 billion each per year in FDI from
the EU, while Morocco has increased its share of total EU FDI, from 6 % in
2000 to about 16 % in 2009 (Zachmann et al. 2012). Host country statistics
reveal that in Algeria, Egypt and Libya, most FDI went into the petroleum
industry, while FDI flows to the manufacturing sector were much smaller
(between 4 % and 8 % of the total). The EU is the leading investor
(based on announced projects listed at www.animaweb.org) in the Southern Rim,
followed by the Gulf countries. The strong role of the EU can be attributed to
its geographical proximity and historical ties with the Southern Rim: France,
Italy and Spain have retained strong links with North African countries, while
British firms are in a strong position in Egypt (Zachmann et al. 2012).
Significant FDI liberalisation measures since the mid-2000s have given a boost to
FDI, in particular in 2006–2008. Nonetheless, the upswing was followed by
setbacks, first in the form of the global crisis and then, in 2011, the events
of the Arab Spring. The revolutions interrupted a period of rapid economic
growth and had a negative impact on both trade and FDI.

Economic
reforms to make Southern Rim countries more attractive to FDI have included
privatisations in the telecommunication and banking sectors, in particular
around 2005/2006. In addition, the influx of petrodollars from the Gulf States
has pushed up prices and activity in the real estate sector. In Egypt for
example, increasing FDI in the energy and service sectors followed a policy
change in 2006, when some state-owned assets were privatised and foreign
investors gained more access. Similar policy changes took place in Tunisia,
triggering a rise in FDI in 2006. But even in those two countries, several business
sectors remain largely off-limits to foreign investors, mainly media, air
transportation and natural resources.

Another
way to look at the development of foreign investment is to see when and where
new greenfield projects have been announced. The number of greenfield FDI
projects undertaken by EU-based MNCs reached a high in 2008, when it was higher
than in any of the three years before or since. Whilst the impact of the
current crisis has so far been limited, the number of new projects has declined
in each of the past three years. With a fifth of all projects, Germany is the Member
State investing the most in the Rim, followed by France and the UK. Over the
last eleven years, the main focus of investments by EU MNCs has been Russia (47 %
of all EU projects and 51 % of total EU pledged investment). Ukraine
attracted much less FDI from MNCs in the EU: 11 % of the projects and 6 %
of the investment capital, which is relatively little considering the size of
the economy. In the Western Balkan countries, especially Serbia, there have been
a remarkably high number of projects relative to their size. Among the Southern
Rim countries, Morocco and Tunisia also have relatively numerous projects in
different industries, confirming that these countries have a comparatively
liberal attitude to FDI. EU Member States have been involved in more than 70 %
of the greenfield investment projects in Serbia, Tunisia, Morocco and BiH. While
Germany, Austria and Italy were the main investors in the Western Balkan
countries, France and Spain were important investors in Morocco, and France by
far the most frequent investor in Tunisia. Egypt is a special case, as it
combines a late opening of a large market with an important oil sector. The other
big oil producers in the European neighbourhood – Azerbaijan, Algeria and Libya
– attracted a small number of high-capital projects. The other Rim countries
are either too small or provide a less liberal environment to attract FDI from
EU MNCs on a big scale; most of their new FDI projects tend to come from
historical and geographical allies.

Difficult
local business conditions (cf. Section 6.2 above) are the main obstacle to
FDI. However, reforms undertaken since the early 2000s have made it easier to
do business in several countries and have contributed to an upswing in FDI.
Morocco, Tunisia and Serbia, but also the other Western Balkan countries, have
been successful in this regard and have attracted FDI in the manufacturing
sector as well as a relatively high number of greenfield investment projects,
often involving SMEs. EU policies fostering trade and FDI and supporting the
liberalisation process have been beneficial for both parties, and for MNCs and
SMEs alike. Supporting open and fair competition and shaping a transparent and
predictable business environment could provide more opportunities for further
FDI and SME development in Rim countries.

Apart
from the business environment, the investment risk of the destination country
is also a factor to consider and has to be weighed against the expected return
on the investment. According to the latest country risk assessment published by
Coface, only two Rim countries, Norway and Switzerland, are in the lowest risk
category (A1). Israel is rated third in terms of risk, marginally ahead of
Morocco and Tunisia. Libya is the Rim country where it is most risky to invest.
BiH, Moldova, Syria and Ukraine are also rated as high-risk countries for
investment, but slightly less risky than Libya (Coface 2012).

              6.5. Southern Rim: fostering North-South and South-South economic
integration

The
Euro-Mediterranean Partnership gained momentum in 1995 with the Barcelona
Declaration and the established goal of a common area of peace, stability and
shared prosperity around the Mediterranean. The current goal is the creation of
a deep Euro-Mediterranean free trade area, aimed at substantial trade
liberalisation both between the EU and Southern Rim countries (North-South) and
between Southern Rim countries (South-South). Relations between the EU and the
Southern Mediterranean are currently organised mainly through bilateral
Euro-Mediterranean association agreements (apart from Syria and Libya). The
Association Agreements with Jordan, Egypt, Israel and Morocco have been revised
based on the 2005 Rabat Roadmap for Agriculture and the Euro-Mediterranean
ministerial mandate to proceed with further trade liberalisation in the areas
of agriculture, processed agriculture and fisheries. In these areas, the new
trade arrangements negotiated in 2008–2011 have led, or will lead, to a
significant opening of agro-food markets on both sides of the Mediterranean. A further
leap forward in Euro-Mediterranean cooperation took place on 14 December 2011,
when a fresh round of trade negotiations was launched with Egypt, Jordan,
Morocco and Tunisia with the aim to establish deep and comprehensive free trade
agreements (DCFTAs) which will go beyond the mere removal of tariffs and cover
all regulatory issues relevant to trade, e.g. investment protection,
intellectual property rights, competition and public procurement. Moreover, in
2012 Jordan and Tunisia joined the European Bank for Reconstruction and Development
(EBRD). The Bank will be able to invest up to EUR 2.5 billion a year
across the Southern Rim, following the recent decision to extend its activities
to the Southern and Eastern Mediterranean. At the same time, loans from the
European Investment Bank (EIB) are guaranteed by the EU to all Southern Rim
countries except Syria.

The EU
will also support capacity building and intends to pay particular attention to
measures to enhance regional economic integration, in particular the process
launched within the framework of the Agadir Agreement (FTA between Egypt,
Jordan, Morocco and Tunisia). Since 1996, the Commission has coordinated the
Euro-Mediterranean industrial cooperation process, with the aim to spur
entrepreneurship and improve the business environment in the Mediterranean
neighbouring countries. This process strengthens Euro-Mediterranean economic
integration and helps companies, in particular SMEs, on both sides of the
Mediterranean to start, grow, export and do business together in a safe,
predictable, transparent environment. The Commission has stated its intention
to upgrade the existing Euro-Mediterranean Charter for Enterprise (European
Commission et al. 2008) into a Euro-Mediterranean Small Business Act and to
extend EU cross-sector and sector-specific networks and actions to Southern
Mediterranean partner countries (European Commission and High Representative
2012a).

Fostering
regional (South-South) economic integration is one of the key objectives of the
Euro-Mediterranean industrial cooperation and trade partnership, and an
essential element in the move towards establishing a fully-fledged
Euro-Mediterranean free trade area. However, regional economic integration
between Southern Mediterranean countries is still limited: intra-regional trade
accounts for a small fraction of the total trade of Southern Rim countries (6 %
of exports, 5 % of imports); many of the borders are either closed or
subject to burden­some procedures, and there is little infrastructure in place
for South-South logistics. In spite of progress and reforms made (cf. European
Commission et al. 2008), SMEs still face extraordinary challenges both in access
to finance, starting up new businesses and in maintaining or extending existing
businesses. At the same time SMEs are of fundamental importance in the Southern
Rim region in at least two specific areas: job creation and economic diversification.
Appropriate financing of SMEs is a precondition for a more dynamic development
of the region. To that end the European Commission has established a special
instrument to foster financing of the private sector, including SMEs. Both the
EIB and the EBRD intend to intensify their activities in Southern Rim
countries.

              6.6. Eastern Rim: hesitant integration

At
present, the main institutional arrangements underlying relations between the
EU and Eastern Rim countries are bilateral partnership and cooperation
agreements (PCAs). As regards the economy, PCAs aim at fostering trade, ensuring
a level playing field for investments through the principle of ‘national
treatment’ (non-discrimination of foreign investments), and promoting
cooperation in a number of priority areas. Most PCAs do not envisage a free
trade regime between Eastern Rim countries and the EU but offer a ‘most
favoured nation’ (MFN) treatment of exports from Eastern Rim countries to the
EU.

Except
for Russia, all Eastern Rim countries are also party to the Eastern Partnership
(EaP) initiative launched in May 2009. The EaP aims to ‘create necessary
conditions to accelerate political association and further economic
integration’ of Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine with
the EU. Cooperation within the EaP framework has concentrated on four broad
areas: democracy and governance, economic integration, energy security, and
contacts between people (including visa liberalisation). Within these four
areas, a number of flagship initiatives have been launched: on integrated
border management, support for SMEs, energy efficiency, civil protection, and
the environment. The task now is to press ahead with the negotiation of AAs
with four of the six EaP partners, including DCFTAs where appropriate, and to
enhance the mobility of people through visa facilitation and re-admission
agreements, as well as gradual steps towards visa liberalisation..

The
current EU strategy towards EaP countries is to negotiate DCFTAs, as part of
broader Association Agreements. The purpose is to integrate EaP countries into
the EU single market in trade-related areas, to the extent justified by their
economic profile and level of development. In December 2011, DCFTA negotiations
were completed with Ukraine and opened with two other EaP countries: Georgia
and Moldova (European Commission and High Representative 2012a). Armenia followed
suit in 2012. As regards Azerbaijan, its WTO accession is a precondition for
any future tightening of relations, therefore current negotiations on an Association
Agreement merely include an update on the trade part of the PCA (European
Commission and High Representative 2012 b). As regards Russia, an
agreement on greater compatibility in the updated PCA is a precondition for
further deepening of EU-Russia trade relations on a preferential basis. A free
trade agreement (rather than a DCFTA) is a long-stated common objective but has
become more difficult to pursue in the short to medium term in the light of the
customs union between Russia, Kazakhstan and Belarus.

The
aims of the DCFTAs are to liberalise trade in goods and services and ensure an
approximation of legislation to EU standards in areas that have an impact on
trade, such as competition policy, public procurement, customs and trade
facilitation, technical barriers to trade, sanitary and phytosanitary rules, sustainable
development, and intellectual property rights. The idea is to create, through
the adoption of these reforms, a favourable business climate in order to
accelerate the flow of EU FDI into the country, as well as to boost exports to
the EU of products that do not currently meet essential EU safety requirements (De
Gucht 2011).

DCFTAs
are expected to have significant and positive effects on EaP economies because
of the potential benefits of the structural reforms that they require. Francois
and Manchin (2009) found that a simple FTA with the EU would lead to a decline
in the GDP of the Commonwealth of Independent States of between 1.1 % and
1.4 %, depending on whether or not trade in agricultural and food products
is liberalised. In contrast, a DCFTA with the EU would boost their GDP by 1.2 %.
Maliszewska et al. (2009) also expected deep integration with the EU to have
positive effects on the EaP countries, with the greatest benefits for Ukraine,
whose GDP would be 5.8 % higher in the long term, followed by Armenia (3.1 %
higher), Azerbaijan (1.8 %) and Georgia (1.7 %). These overall gains
would, however, be accompanied by profound structural changes and the output of
some sectors would go down drastically. The Institute for Economic Research and
Policy Consulting has found that a DCFTA with the EU would increase welfare in
Ukraine by nearly 12 % in the long term – more than twice the figure to be
expected in the case of a simple FTA with the EU (Movchan and Giucci 2011). In
a similar vein, the experience of Turkey, whose entry into a customs union with
the EU in 1995 was accompanied by the approximation of various policies to EU
standards, also suggests strongly positive effects (Togan 2011).

Failure
to conclude DCFTAs would have negative consequences for both sides: the EaP
countries would find themselves stuck in the current trap of low
competitiveness and instability, while at the same time the competitiveness of
EU businesses in the EaP countries would suffer. For instance, the unreformed
(and in many cases corrupt) system of public procurement in EaP countries would
continue to disadvantage foreign suppliers (including those from the EU) and
hamper the development of SMEs.

              6.7. Labour markets and migration

The
impact of increased labour migration from Rim countries is of particular
interest to EU policymakers. The Southern Mediterranean region is recognised as
a region of emigration, with the total number of first-generation emigrants
somewhere between 10 million and 13 million (World Bank 2011 b).
Increasing differences in economy, demography, politics and security matters,
together with its geographical proximity, make the EU the main destination for
migrants from the region. Immigrants from Mediterranean neighbouring countries
represent 20 % of the 30 million immigrants in the EU and 1.2 % of the
total EU population. Following the Arab Spring, the flow of migrants from the
region is expected to rise. Moreover, the region is a transit route for
migrants from other, more distant and even less developed regions.
Consequently, EU migration policy towards this region can be expected to evolve
significantly and gain even greater prominence.

The promotion of the mobility of EaP citizens represents
one of the main commitments made by the EU in the Prague Declaration of the
Eastern Partnership Summit (May 2009) as well as in the Joint Communication on
a new response to a changing Neighbourhood (European Commission and High
Representative 2011) and the subsequent Joint package on delivering a new
European Neighbourhood Policy (European Commission and High Representative
2012a). As a contribution to a more ambitious partnership with its Eastern
neighbours, this commitment builds on the four pillars of the global approach
to migration of the EU: better organising legal migration and fostering
well-managed mobility; preventing and combating irregular migration/eradicating
trafficking in human beings; maximising the development impact of migration and
mobility; and promoting international protection, and enhancing the external
dimension of asylum. The Western Balkan countries, some of which are candidates
or potential candidates for EU membership and most of which (apart from Kosovo)
have recently benefited from visa liberalisation, are experiencing a new
migration development, since their citizens no longer need a visa to travel to
the EU (except for Kosovo citizens).

The
development of migration management systems has been uneven across regions, not
least because of differences in available resources and in the general
development of the quality of public institutions. The links between migration
and employment or education policies remain vague in all countries of the
region (European Training Foundation 2011) but these links are none the less
relevant for their competitiveness. In particular, the high level of migration is
linked to economic hardship and unemployment. Labour migration represents an
alternative mechanism to gain employment and is a reaction on the part of the
population to social and economic crisis and internal conflict.

              6.7.1 The Eastern Rim

The population structure in the Eastern
Rim countries is very heterogeneous: Armenia and Azerbaijan have very young
populations, with the age group up to 14 years accounting for around 30 %,
while this age group represents only 14 % in Ukraine and Russia. Ageing of
the population in these economies will pose a serious risk to welfare systems.
With the exception of Russia, the economic activity rates are below the EU
average of 71 %. A salient feature of the labour market in the Eastern Rim
countries is the high activity rate of females, which in most cases is comparable
to the EU level (and distinctly higher than the Southern Rim).

With
the exception of Russia (and to a lesser extent Ukraine), agriculture is an
important source of income in the Eastern Rim countries, although its share has
been declining everywhere. Agriculture in Moldova, Azerbaijan and Armenia can
barely be considered to be an economic sector (in the sense used in more
developed economies) as the ‘preponderance of subsistence farming on small
scale plots has made this activity a buffer for employment lost during
restructuring of industrial enterprises and small scale farms’ (European
Training Foundation 2011). The relevance of industry is highest in Ukraine and Russia
(cf. European Commission (2009a, 2011a) for discussions of Russian industry),
whereas the industrial base is very small in Georgia and Azerbaijan, accounting
for only 10-13 % of total employment. The share of employment in the
service sector has been rising steadily in Moldova, Ukraine and Russia. In the
latter two countries, the service sector accounts for about 60 % of total
employment. The fragility of the labour markets is highlighted by the high
proportion of self-employment – 64 % in Georgia, 58 % in Azerbaijan,
39 % in Armenia and around 30 % in Moldova. Unemployment has been
relatively low in most Eastern Rim countries. However, given the high
proportion of self-employment (subsistence agriculture) in these countries,
unemployment is probably much higher than official figures suggest (European
Commission 2011 b).

The
latest data available on migrants from the EaP region show that the number of
migrants reached almost 11 million in 2010 – a figure only slightly below
the total stock of migrants from Russia. Among the EaP countries, more than 6
million people emigrated from Ukraine, more than 1 million each from Azerbaijan
and Georgia, and less than 1 million each from Armenia and Moldova. The
preferred destinations for Eastern Rim migrants are Russia and the EaP region
itself, which hosts more than half of all EaP migrants.

Migrants
from Eastern Rim countries make up 12 % of all migrants in the EU (in
absolute numbers, the EU hosts around 1.4 million migrants from the EaP region
and 1.1 million from Russia). The EaP country with the largest share of
immigrants in the EU is Moldova. The EU Member States with the largest number
of Eastern Rim migrants are Germany, Poland, Spain, Greece, Italy, Estonia and
Latvia.

Mobility
Partnerships aiming at enhancing and promoting mobility of people have been
concluded between the EU and Moldova, Georgia and Armenia. Negotiations with
Azerbaijan are ongoing.

              6.7.2 The Southern Rim

A
prominent feature of the Mediterranean neighbouring countries is the high share
of young people in their populations: almost a third of them are younger than
14. As a consequence, and notwithstanding rapidly declining birth rates, the
working-age population in the region will continue growing in coming decades.
The large influx of new labour market entrants, combined with lower rates of
workers retiring and low job creation, has put enormous pressure on Southern
Rim labour markets and will continue to do so. Thus, job creation will remain a
top priority in the coming years if the countries are to retain or reduce their
current unemployment levels. Estimates made by international organisations of
the need for additional jobs in the next decade range from 25 million jobs
(MENA-OECD Investment Programme) to 50-75 million jobs (World Bank 2011c). Such
high rates of job creation would require annual GDP growth rates of 6.5 %
or more, which is hardly realistic given the structure and poor competitiveness
of the economies.

Activity
rates are very low in the region and have grown only modestly (if at all). This
is mainly because of low rates among females, ranging from only 14 % in
Syria to 32 % in Libya (OECD and International Development Research Centre
2012). Israel is the only country in the region where female labour force
participation (61 %) is comparable to EU levels. Employment patterns by broad
economic sector differ substantially across the region, but agriculture is
still an important employer almost everywhere. Industrial employment is highest
in Tunisia (35 %) and Syria (32 %), while Israel, Jordan and Morocco have the
lowest shares (around 20 % each). A breakdown of service-sector employment
shows that administration (government services) accounts for more than half of
the sectoral employment in Jordan, Algeria, Syria and Egypt, while its share is
relatively small in Morocco. As regards market services, the major sectoral
employers are trade, tourism and communications (World Bank 2011c). Together
with construction and, in some cases, agriculture, these sectors have also been
the major drivers of employment creation in recent years. The public sector –
including government agencies, military and state-owned enterprises – is the
preferred source of employment for graduate (female) workers in the Mediterranean
neighbouring countries, accounting for up to 35 % of total employment.
Employment in the public sector offers higher wages, employment protection,
shorter working hours and other social benefits. In the past, the rise of
public sector employment was driven by social contract obligations guaranteeing
all graduates a state job; this led to a concentration of highly skilled people
in the state sector. Consequently, ‘guaranteed employment without concern
for productivity led to the prevalent rent-seeking behaviour among graduates
and created strong disincentives for work in the productive sectors’ (European
Commission 2010 b). Governments have therefore had to terminate the system
of guarantees. Despite the reforms, however, the public sector wage bill still
accounts for 8-10 % of GDP in most countries (European Commission 2011 b).

In
2010, the unemployment rate in the Mediterranean neighbouring countries was
around 10 %. However, unemployment among people with a university or
secondary education is considerably higher than among people with little or no
education, and in some Southern Mediterranean countries the time between
completing university education and finding employment can be as long as eight
years. This represents a particular challenge, even though the number of
university graduates remains very low in the region. Youth unemployment is
considered to be a major challenge and is highest in Palestine (39 %) and
Tunisia (31 %). It is lower (14-18 %) in Israel, Lebanon and Morocco and around
20 % in other Southern Rim countries. The labour markets of the Southern
Rim countries have been less affected by the euro area crisis than most EU
Member States or the Western Balkan countries (European Commission 2011 b). The crisis mainly affected
export-oriented firms in certain Southern Rim countries (Egypt, Libya, Syria
and Tunisia) as well as migrant workers. On top of the enormous pressure of
young cohorts entering the labour market, the revolutions of the Arab Spring have
brought about additional increases in unemployment as numerous migrants have
returned (e.g. from Libya) and the private sector has laid off temporary
workers (Galal and Reiffers 2011).

Southern
Rim countries have very dynamic populations and high migrant numbers, with
several of them serving not only as sending and receiving countries, but also
as transit countries. Before the Arab Spring, there were over 12 million
Southern Rim migrants, more than from any other Rim region, with Egypt and
Morocco receiving the greatest numbers of migrants. The EU is the main
destination region, hosting more than 40 % of migrants from the Southern
Rim, particularly from Morocco, Algeria and Tunisia. Moreover, almost a third
of migrants from Lebanon and Libya have moved to the EU, while only 7 % or
less of migrants from Egypt, Israel and Jordan find their way to the EU. The
main destination countries for Moroccan migrants are France, Italy, Belgium,
Germany and the Netherlands, while more than 80 % of Algerian and Tunisian
migrants are in France.

The
flow of migrants from the Southern Rim countries to the EU was on the increase
until 2008, when it reached 180.000. However, as in the case of Eastern Rim
migrants, the flow from the Southern Rim countries has declined significantly
in the wake of the recent financial crisis. The
turmoil of the Arab Spring generated a fresh wave of irregular migration,
particularly from Tunisia, where attempts to reach Italy and France increased
significantly in late 2010 and early 2011. Fears over sizeable movements of
irregular immigrants induced EU governments to sign bilateral agreements with
potential migration countries, with a view to halting the irregular crossing of
coastal borders. Moreover, climate change and environmental disasters have
generated another flow of migrants from outside the Rim who have been forced to
migrate because of unsustainable conditions at home.

Cooperation
on migration and mobility related issues between the EU and Southern Rim is
very intense, in particular with Morocco and Tunisia with which the EU is
negotiating Mobility Partnerships in order to enhance mobility and strengthen
cooperation on migration related issues. Cooperation with Egypt and Libya will
intensify in the future, leading to possible Mobility Partnerships, once the
internal situation of those countries so allows.

              6.7.3 Western Balkans

Almost
the entire Western Balkans region is characterised by demographic contraction,
high outward migration and ageing populations. Only Albania and Kosovo have a large
share of the population in the age group up to 14 years. The entire region also
has low activity rates, with extremely low levels in Kosovo (below 50 %) and in
BiH, while in Albania and Serbia the rate is about 60 %. Female
participation in the labour force is particularly low in specific ethnic groups
across the region, and in particular in Kosovo and BiH. The region has a high
share of agricultural employment (Albania, with 55 % of its total
workforce employed in agriculture, is an extreme case in this respect and is
similar to Georgia and Morocco). Employment in industry is highest in BiH (31 %)
and about 25 % in Serbia and Kosovo. The service sector is less developed
in the Western Balkan countries, accounting for about half of total employment
in Serbia and BiH, and only 37 % in Albania. By contrast, the service
sector represents a very high proportion of the labour force in Kosovo.

Unemployment
in the Western Balkans is very high – in fact higher than in any other Rim
region. Kosovo and BiH have the highest rates of unemployment in the region.
Albania is the only country where unemployment has remained flat in recent
years, possibly helped by a long tradition of outward migration in combination
with relatively stable employment in agriculture. Unemployment has a
disproportionate impact on young people. Like in some Eastern Rim countries,
there is a sizeable and persistent regional imbalance in unemployment, which
suggests that there are major barriers to regional labour mobility. In many
cases young people lack the skills and professional experience for employment,
so their options are to emigrate or enter the informal economy (Vidovic 2011).
Long-term unemployment has become a persistent and salient feature of the
Western Balkan labour markets and is much more severe than in other transition
economies. However, it can be assumed that the high reported rates of long-term
unemployment are distorted and hide large flows between the formal and informal
sector.

There
is a long history of migration in the Western Balkans as most Balkan countries
share common borders and cultural ties with EU Member States. More recently,
wars have created additional migration by forcing refugees to flee to other
countries. The total number of migrants from the Western Balkans is around 4.5
million, mainly from BiH and Albania, each with more than 1.4 million migrants.
While 85 % of all Albanian migrants have migrated to the EU, only half of
the migrants from BiH have chosen the EU as their destination. Visa
liberalisation in 2011 contributed to an intensification of circular migration
and to a reduction in illegal migration to the EU. There have been fewer cases
of Albanian migrants illegally crossing the EU border or overstaying their
visas in Member States. However, there has been an increase in the number of
applications for international protection (asylum) submitted in the EU, particularly
from Serbia and Albania. The difficult economic
situation in Greece has forced many Albanians to return home, for good or
temporarily and will continue to exert pressure on Western Balkan labour
markets.

              6.7.4 Norway, Switzerland and Liechtenstein

All
three countries have experienced population growth over the past decade. Their
labour markets are characterised by low unemployment and high activity and
employment rates, the latter reaching over 75 %. In all three countries,
unemployment is very low compared with the EU – another example of the diversity
of the Rim.

              6.8. Remittances

              6.8.1 The Eastern Rim

Migration
and remittances both show an increasing trend over the last 20 years,
generating significant welfare gains either for the home country of the
migrants or for the migrants themselves. In 2000, remittances sent to the EaP
group of countries amounted to around USD 769 million, while in 2011
the estimated amount was 16 times higher, at around USD 12.3 billion.
Moldova has the highest share of remittances to GDP (23 %), and remittances are
among the main contributors to developments on its labour market.

              6.8.2 The Southern Rim

In 2011, the overall amount of
remittances was around USD 33 billion, three
times higher than in 2001. The main receiving countries were Lebanon and Egypt.
In the light of persistent unemployment in Europe and precarious employment
prospects for existing migrants, as well as rigid immigration policies, there
is a risk that remittances will decrease in future years (Mohapatra et al.
2011a, b). In Libya, Tunisia and Egypt, numerous migrants returned home or
were deported back to their country of origin during the Arab Spring. Such
developments might also negatively affect the future flow of remittances to the
country of origin, holding back growth in the region (Ben Mim and Ben Ali 2012).

              6.8.3 Western Balkans

Remittances
strongly affect the economic development in the Western Balkans, in particular
in Kosovo and BiH, where the share of remittances to GDP is 18 % and 13 %
(World Bank 2011 b). In 2011,
the flow of remittances to the Western Balkan countries reached nearly
USD 10 billion, three times more than in 2002. As in other regions,
most of the Western Balkan countries recorded a decline in the flow of
remittances from 2008 to 2009, but from 2010 to 2011 there was again an
increase (+6 %). The difficult economic situation in the euro area (particularly
in Greece, Spain and Italy) raises concerns that there will be less demand for
migrant workers, which might trigger a massive return migration and depress
flows of remittances accordingly. Remittances to Albania may keep falling
if migrants continue returning from Italy and Greece. At the same time, the
positive effects in terms of migrants returning to their country of origin with
new skills, knowledge and capital, must not be ignored.

              6.9. Labour migration and EU competitiveness

One of
the policy objectives of the Europe 2020 strategy is to reinforce EU
competitiveness in the international arena. In view of recent developments in
the EU, in particular its ageing population and shrinking labour
force, potential labour market shortages – in terms of numbers as well as
skills – put the competitiveness of the EU at risk. In this context, labour
migration has gained higher attention in the policy debate as it could
contribute to meeting the objectives of sustaining employment growth, reducing
un­employment, satisfying labour demand for highly skilled workers and filling
sectoral labour market shortages with migrant workers (European Commission 2009a).
The 3rd EU Annual Report on
Immigration and Asylum underlines the positive contributions that migration makes
and will need to bring in order for the EU to grow and continue to thrive
(European Commission 2012 b).

The
economic crisis and increase in unemployment in the EU have forced several Member
States to introduce severe austerity measures. At the same time, despite the
sharp rise in unemployment in several Member States, labour shortages persist
for various reasons, for instance unattractive working conditions, lower wages
offered by employers, and limited geographical mobility (EMN 2011). Meanwhile, qualitative
shortages are the result of insufficient numbers of workers with appropriate
qualifications and skills. Moreover, migration within the EU, particularly
migration from and between the 2004/2007 accession states, has generated labour
market shortages also in several of these Member States.

In
contrast, demographic trends indicate that the Southern Rim countries will
experience a significant increase in the working-age population, which will
exceed demand on the domestic labour market. It is highly likely that a
considerable number of young, and particularly well-educated, people will not
find a place on the domestic labour market and will be forced to migrate.
Several Member States have adopted national strategies to mitigate the demand
for labour through the migration of third-country nationals, and in particular
migrant workers from Rim countries. Available data on third-country workers in
the EU suggest that Rim countries account for a large share of migrants and
that the contribution of migrant workers from the Rim countries, especially
from the Western Balkans, Russia and Ukraine, is very important for a number of
Member States.

              6.10. Policy implications

Countries
belonging to the Rim are extremely diverse. Their diversity is multidimensional
(geographical, socio-economic, political, cultural and religious) and each
individual dimension has important implications for EU policies towards the
region, for EU institutional relations with individual Rim countries, and for
Rim countries themselves – including their competitiveness.

More
specifically, with respect to the institutional relations between the EU and
the Rim, the key question is whether the current EU approach – aiming at the
conclusion of bilateral DCFTAs with the countries in the Rim able and willing
to do so – is optimal and sufficient (or even appropriate) for every country
and society in such a diverse group. Evidence suggests that for sustainable
development, there is no alternative to domestic policy reform as outlined in
the DCFTAs, to boost domestic competitiveness and external trade. Apart from
policies aimed at bilateral trade liberalisation and measures to support the
investment climate in the countries concerned, the DCFTAs and the industrial
cooperation process will also contribute to promoting regional integration and
intra-regional cooperation, in particular as and when the pan-EuroMediterranean
rules of origin allow diagonal cumulation. If duly implemented by the partner
countries, these initiatives would be particularly helpful in the Eastern and
Southern parts of the Rim, where regional fragmentation is particularly
detrimental to further growth.

Regarding
the economic development model, except for in the Advanced Rim, the economic
growth of Rim countries and their progress in catching up have been the result
not of increased exports, but in most cases – apart from energy exporters and
tourist destinations – stem from increasing domestic demand, frequently
financed from transfers (aid and remittances to resource-poor countries). The
growth of industry in the majority of Rim countries, and in the Southern Rim in
particular, has been slower than the growth of GDP. Recent experience in the EU
shows that any pre-crisis neglect in building up a viable trade sector and
sufficiently competitive export capacities tends to aggravate the crisis.
Policies leading to an expansion of the export sector have to take priority,
and the use of different policy instruments (e.g. labour market, investment
promotion, institutional development, entrepreneurial promotion) needs to be
strengthened (Gligorov et al. 2012).

Competitiveness
in the Rim needs to be improved (again, except for the Advanced Rim). This is
reflected in the low intensity of manufacturing exports and insufficient
inward FDI flows. The reasons for this are manifold and related to the
political context, the economic sluggishness (and dependence on slow-growing EU
economies) in general, low employment skills and also the poor business
climate, adversely affecting SMEs in particular. The Eastern Rim has been doing
somewhat better in this respect than both the Western Balkans and the Southern
Rim in a number of business-relevant areas (such as access to finance, use of
foreign technology, labour market regulations and worker skills). Southern Rim
countries are highly heterogeneous; some have made impressive progress while
others are held back by poor competitiveness in industry and technology.
Improving investments in education is key; there is a lack of high-quality,
technology-based teaching and a severe mismatch between the orientation of
students and the needs of the economy, as well as poorly performing secondary
education students. In several countries there can be up to eight years between
completion of university education and taking up employment (European
Commission et al. 2008).

Though
important for the trade surpluses of some EU Member States, the Rim countries
are relatively minor trading partners for the EU as a whole and do not pose any
serious challenge to EU competitiveness. However, the trade asymmetry – the EU
being the main trading partner of Rim countries in most cases – is challenging,
not least for the formulation of EU policies, since any bilateral agreement
will impact more on the Rim than the EU. Trade asymmetry and the underexploitation
of the trade potential arising from geographical proximity should be overcome.
In particular, the proximity of the huge EU market can be thought of as a
locational competitive advantage of the Rim, so far largely unexploited. Each
of the four Rim regions is a focal area in terms of trade flows for at least
one part of the EU. The varying regional specialisation (and interests) of
individual Member States represents another challenge for the formulation of a
uniform and effective EU policy or policies towards the Rim.

Limited
diversification of exports (except for the Advanced Rim) is one of the greatest
stumbling blocks for competitiveness. In spite of attempts to improve the
international competitiveness of the Rim countries – product and labour market
reforms, but also liberalisation efforts and improvements in the business
climate in general – the Rim economies still need to develop the industrial
capacity and the necessary structural flexibility to respond successfully to
external competitive pressures. These drawbacks result in high adjust­ment
costs and low gains from liberalisation in terms of an increased emergence of
new firms and new export products.

European
FDI plays a crucial role in the Rim region. FDI by European companies,
including SMEs, can exploit locational benefits, even though the poor business
environment in the Rim limits FDI flows. Improved conditions for doing business
benefit local SMEs and EU investors alike. SMEs have benefited in countries
like Serbia, Morocco and Tunisia, all of which have managed to attract a number
of greenfield FDI projects in different industries. Further policy reforms
should take place in order to open the remaining restricted sectors in the Rim
countries. Open and fair competition, breaking local (often state-supported)
monopolies, could increase opportunities for further FDI flows and the
development of SMEs (European Commission 2011c).

A major
impediment to the competitiveness of the Rim is regional fragmentation. Even
within the four Rim regions there are many barriers to trade and business in
general (the persisting frozen or open conflicts are obviously unhelpful as
well). Numerous trade barriers exist in both the Eastern and Southern parts of
the Rim. In the Southern Rim, the limited intra-regional integration is viewed
as the key obstacle to FDI, trade diversification and growth. In the Eastern
Rim, attempts at a revival of Russian-led regional integration (the customs
union between Russia, Belarus and Kazakhstan) have had the effect that the prospects
of a free trade agreement between the EU and Russia – a long-stated objective
on both sides – should now be seen in a long-term perspective. The continuing
bilateral ‘hub-and-spoke’ trade arrangements between the EU and the Rim
resemble the pre-accession arrangements which the EU concluded with accession
countries from Central and Eastern Europe during the 1990s (Baldwin 1994).
However, without a strong anchor in the form of future EU membership, it is
important to maintain a high level of ambition in EU trade agreements with the
neighbourhood countries to foster reforms, regional integration and a
sustainable development of the Rim (Dreyer 2012).

Demography
and labour market developments are among the crucial areas affecting
competitiveness, yet frequently neglected in this context. The Rim is
characterised by large informal sectors, labour market segmentation, high
unemployment and large-scale migration. A number of differences and common
features can be identified:

·
Because Armenia,
Azerbaijan, Albania, Kosovo and the Mediterranean neighbouring countries all
have a high share of young people in their populations, large cohorts are
entering the labour market each year. All other countries are faced with ageing
(and often shrinking) populations, exerting serious pressure on the welfare
systems and potentially holding back competitiveness (as it is in the EU).

·
Activity rates are below 50 %
in all Southern Rim countries and Kosovo. In Eastern Rim countries, labour
force participation is similar to the 2004/2007 accession states and can even
exceed the EU average.

·
The employment gap between
males and females is substantial in some Western Balkan countries and in the Mediterranean
neighbouring countries. On the other hand, female labour force participation in
the Eastern Rim countries is traditionally high, on a par with that in the EU.

·
With the exception of
Russia and Ukraine, Eastern Rim countries have a high share of persons in
vulnerable employment. Among Southern Rim countries, Morocco stands out as
about half of its workforce have vulnerable jobs. There is also an important
north/east/south divide in the educational attainment and qualification
structure of employment, with more highly educated workers in the north and east
than in the south.

Given
the irreversible nature of the ageing workforce in the EU, the potential of
human resources in the Southern Rim represents an opportunity for sustaining
employment growth and international economic competitiveness in the EU as well
as in the Southern Rim in coming decades. The promotion of circular migration
and various programmes that induce temporary migration is a challenging way of
satisfying labour shortages in the EU. It should not be neglected.

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 7.     Statistical annex

              7.1. Sectoral
competitiveness indicators

Explanatory notes

Geographical coverage: all indicators refer to
EU-27

Production index.[62] The
production index is actually an index of final production in volume terms.

Labour productivity: this indicator is
calculated by combining the indexes of production and number of persons
employed or number of hours worked.[63] Therefore,
this indicator measures final production per person of final production per
hour worked.

Unit Labour Cost: it is calculated from the
production index and the index of wages and salaries and measures labour cost
per unit of production. “Wages and salaries” is defined (Eurostat) as “the
total remuneration, in cash or in kind, payable to all persons counted on the
payroll (including homeworkers), in return for work done during the accounting
period, regardless of whether it is paid on the basis of working time, output
or piecework and whether it is paid regularly wages and salaries do not include
social contributions payable by the employer”.

Relative Trade Balance: it is calculated, for
sector “i”, as (Xi-Mi)/(Xi+Mi), where Xi and Mi are EU-27 exports and imports
of products of sector “i” to and from the rest of the World.

Revealed Comparative Advantage (RCA):

The RCA indicator for product “i” is defined as
follows:

where: X=value of exports; the reference group
(‘W’) is the EU-27 plus 105 other countries (see list below); the source used
is the UN COMTRADE database. In the calculation of RCA, XEU stands for
exports to the rest of the world (excluding intra-EU trade) and XW measures
exports to the rest of the world by the countries in the reference group. The
latter consists of the EU-27 plus the following countries: Albania, Algeria,
Azerbaijan, Argentina, Australia, Bahamas, Bahrain, Armenia, Bermuda, Bhutan,
Bolivia (Plurinational State of), Bosnia Herzegovina, Brazil, Belize, Bulgaria,
Myanmar, Burundi, Belarus, Cambodia, Canada, Cape Verde, Sri Lanka, Chile,
China, Colombia, Costa Rica, Croatia, Dominica, Dominican Rep., Ecuador, El
Salvador, Ethiopia, Fiji, French Polynesia, Georgia, Gambia, Occ. Palestinian
Terr., Ghana, Guatemala, Guyana, China, Hong Kong SAR, Iceland, Indonesia,
Israel, Côte d'Ivoire, Jamaica, Japan, Jordan, Kenya, Rep. of Korea,
Kyrgyzstan, Lebanon, China, Macao SAR, Madagascar, Malawi, Malaysia, Maldives,
Mali, Mauritius, Mexico, Other Asia, Rep. of Moldova, Montenegro, Oman, Nepal,
Aruba, New Caledonia, New Zealand, Nicaragua, Niger, Nigeria, Norway, Pakistan,
Panama, Paraguay, Peru, Russian Federation, Rwanda, Saint Vincent and the
Grenadines, Saudi Arabia, Senegal, Serbia, India, Singapore, Viet Nam, South
Africa, Zimbabwe, Suriname, Switzerland, Syria, Thailand, Togo, Tonga, Trinidad
and Tobago, Tunisia, Turkey, Uganda, Egypt, United Rep. of Tanzania, USA,
Burkina Faso, Samoa, Zambia.

Statistical nomenclatures: the indicators in
tables 7.1 to 7.6 are presented at the level of divisions of the statistical
classification of economic activities in the European Community (NACE Rev.2[64]), while those
in tables 7.7 and 7.9 are presented in terms of divisions of the statistical
classification of products by activity (CPA). Table 7.10 uses extended balance
of payments services classification. In terms of data sources: tables 7.1 to
7.6 are based on Eurostat’s short-term indicators data. Tables 7.7, 7.8 and 7.9
are based on United Nations’ COMTRADE. Table 7.10 is based on IMF balance of
Payments. Royalties and license fees were not included as it is not related to
a special service activity.

Table 7.1 - EU-27 - Industry production  index,
annual growth rate (%)

N/A:
data not available.

Source: Eurostat.

Table 7.2 - EU-27 - Number of persons employed,
annual growth rate (%)

N/A: data not available.

Source: Eurostat.

Table 7.3: EU-27 - Number of hours worked,
annual growth rate (%)

N/A: data not available.

Source: Eurostat.

Table 7.4: EU-27 - Labour productivity per
person employed, annual growth rate (%)

N/A: data not available.

Source: Eurostat.

Table 7.5: EU-27 - Labour productivity per hour
worked, annual growth rate (%)

N/A: data not available.

Source: Eurostat.

Table 7.6: EU-27 - Unit labour cost, annual
growth rate (%)

N/A: data not available.

Source: Eurostat.

Table  7.7: EU-27 - Revealed comparative
advantage index

Note: 
there was a transition from NACE REV 1 to NACE REV 2, therefore the data are
only available from 2007.

Source: own
calculations using Comtrade data.

Table 7.8: EU-27 - Relative trade balance
(X-M)/(X+M)

Note: 
there was a transition from NACE REV 1 to NACE REV 2, therefore the data are
only available from 2007.

Source: own
calculations using Comtrade data.

Table 7.9.1:
Revealed comparative advantage index in manufacturing industries in 2010 - EU
countries, Japan and Brazil, China, India and Russia.

Source: Own
calculations using COMTRADE data.

Table 7.9.2:
Relative trade balance (X-M)/(X+M) in manufacturing industries in 2010 - EU
countries, Japan and Brazil, China, India and Russia.

Source: Own
calculations using COMTRADE data.

Table 7.10:
Revealed comparative advantage index in service industries in 2010- EU countries,
US, Japan and Brazil, China, India and Russia.

Source: IMF, OECD.

[1]           This chapter is based on the
background report, Falk et al. (2012) ʻFDI flows and impacts on the
competitiveness of the EU industryʼ.

[2]           http://register.consilium.europa.eu/pdf/en/07/st10/st10113.en07.pdf,
http://ec.europa.eu/development/icenter/repository/EAS2007\_joint\_strategy\_en.pdf

[3]         FDI in R&D has been found
even more concentrated (European Commission, 2012).

[4]            A particularity of
the FDI from Western Asia, however, is that much of it constitutes investments
by Sovereign Wealth Funds (SWFs) which must be assumed to have little impact on
the EUʼs real economy in general and to EU competiveness in particular
because SWFs do not normally become involved in the management of the firms in
which they take a stake. The appetite of SWFs for FDI engagements in the EU
seems to have lasted only until 2009 (UNCTAD, 2011). As a consequence, EU
inflows from Western Asia dropped to a mere EUR 400 m in 2010.

[5]         For example, EU inflows from
South America and Sub-Saharan Africa amounted to approximately EUR 1.7 bn
annually in 2008-2010 while inflows from South Asia (mainly India) and the
ASEAN countries amounted to EUR 1 bn and EUR 1.3 bn
respectively. For China Eurostat reports inflows of only EUR 80 m for
2008-2010.

[6]           According to the
Ministry of Commerce of China. However, Eurostat reports only EUR 100 m for
2010. The difference is partly explained by the fact that for instance, for
confidentialility reasons Sweden did not report data on inflows from China.

[7]            The strong increase
in Chinese FDI flows to the EU in 2010 is mainly but not entirely due to the
purchase of  the Swedish car company Volvo by China's car manufacturer Geely.

[8]           Crossborder
Greenfield investment data stem from the fDi Intelligence, service provided by
The Financial Times Ltd (also called fDi database) See
http://www.fdimarkets.com.

[9]           This is a natural
path in which FDI follows previous export activities. See Conconi, Sapir and
Zanardi (2010). In the case of China or India, however, to the extent that
trade is based on their specialisation in low-tech, low-wage sectors, the step
from exports to FDI may be less straightforward.

[10]          The overwhelmingly
large FDI stocks of the financial sector (EUR 1357 bn) include the activities
of Special Purpose Entities. ʻOther business activitiesʼ (EUR 430 bn)
include business and management consultancy activities,i.e. FDI undertaken by
holding companies. When including other business activities in total inward FDI
the share of services increases significantly (64%) and that of manufacturing
falls below 30%.

[11]          This calculation
again excludes the financial sector.

[12]             Ernst &Young
(2012).

[13]             The backround
study (Falk et al., 2012)  provides a summary of the  literature on the FDI
determinants.

[14]             The
main contribution of this analysis is to investigate the determinants of both total
FDI stocks and greenfield FDI flows using panel data methods that make it
possible to control for fixed host and homecountry and common time effects. In
addition, the presence of zero values of FDI flows is taken into account by
using a variant of the Poisson regression model.

[15]          Most EU countries have
a low (under 0.1) FDI Restrictiveness Index (OECD).

[16]          Similar results are found when
focusing on R&D only. In that case however, the labour costs proved to be a
less important determinant. (European Commission, 2012).

[17]          This overview is
based on various issues of UNCTAD's World Investment Report.

[18]          According to Wren
and Jones (2011) countries such as the UK and France spend half of their
regional grant budgets on attracting FDI flows.

[19]          Information is based
on the websites of the investment promotion agencies of the EU-27 countries.

[20]          The sample is
limited to the EU-15 countries due to severe data limitations and the very low
coverage of MNEs with respect to a number of EU-12 countries.

[21]          To test the
significance of the results the Kolmogorov-Smirnov stochastic dominance test is
applied along with the more formal econometric tests based on the zero-inflated
negative binominal (ZINB) count data model.

[22]          See the background
study, Falk et al. (2012).

[23]          The data consist of
a sample of 29 EU and EFTA countries plus Turkey for the period 1985-2010 where
data are measured as five-year averages.

[24]          Unreported results
show that the growth effect of FDI increases with the relative level of GDP per
capita to the country with the highest GDP per capita.

[25]          Note that inward
FATS statistics and balance of payments based FDI flows are not directly
comparable since FATS is based on the 50.1% rule (share of the voting rights) while
FDI is based on 10% voting power. The number of countries for which data are
avaialbe is limited to 20-22, depending on the sectors.

[26]          Except Ireland, Spain and Portugal.

[27]          In these high-tech
and medium high-tech manufacturing sectors, the internationalisation of firmsʼ
R&D activities more pronounced than in other sectors (European Commission,
2012).

[28]          Kokko (1992) and
Blomström and Kokko (1998).

[29]          See Barry, Görg and
Strobl, 2004, for Ireland; Pesola, 2011, for Finland; and Martins, 2011, for
Portugal.

[30]        See
also Alfaro et al. (2004): Noorbakhsh and Paloni (2001); Borensztein, De
Gregorio and Lee (1998).

[31]          Sabirianova,
Svejnar, and Terrell (2009); Rodriguez-Clare (1996); Acemoglu, Aghion and
Zilibotti (2006).

[32]          The
background study, Falk et al. (2012) summarises the results of more than 70
studies investigating the effects of FDI published after 2000. The absorptive
capacity hypothesis is confirmed in 12 out of 20 studies, with the relative
productivity level between domestic and foreign firms the most widely used
measure of absorptive capacity.

[33]          Background study,
Falk et al. 2012.

[34]        This section is based on yet
unpublished results from the EU funded project INNO Grips
ENTR-09-11-LOT2.

[35]          The eight EU-10
countries considered are: Bulgaria, the Czech Republic, Estonia, Poland,
Romania, Slovenia and the Slovak Republic.

[36]          Exceptions are
Vahter (2011) for Estonia or Bertschek (1995) and Blind and Jungmittag (2004)
for German firm level data.

[37]
       Process
innovation refers to new or significantly improved production process,
distribution method or              supporting activity.

[38]       This important result
has also been found when analysing specifically R&D investments of firms
abroad (European Commission, 2012). R&D intensities of domestic and foreign
firms are positively correlated. Furthermore, no evidence has been found that
inward R&D crowds out R&D activites of domestic firms. On the contrary
both are found complementary. Reciprocically, there is no evidence that R&D
activities performed abroad are substitutions for similar domestic actitivites.

[39]          The data used here are
based on the Business Environment and Enterprise Performance Survey (BEEPS)
2005 and 2009 provided by the World Bank. The data contains information for the
years 2004 with about 3500 observations for the business enterprise sector. Information
on technology licences obtained from foreign-owned firms in the manufacturing
sector is taken from the BEEPS 2009 survey.

[40]        The share of the EU-12 in
intra-EU-27 stocks is even lower, at around 2% in 2010; it is, however
considerably higher within the EU-12 amounting to 8.7%. More details about the
FDI activities of MNEs from the EU-12 are provided in the next section.

[41]        In the case of Russia, EU investments may
to some extent be overstated because a third of the EUʼs FDI stock in
Russia is owned by Cyprus (which makes it the largest investor) but these flows
are understood to mainly constitute ʻround-trippingʼ capital. ʻRound-trippingʼ
FDI refers to Russian investment channelled back via Cyprus for tax purposes
(Hunya and Stöllinger, 2009). Moreover, these figures also include FDI stocks
owned by Luxembourg which to a very large extent represents financial
intermediation activity. The main results from this analysis are not affected
by these ʻanomaliesʼ.

[42]          Firm level data stem
from the AMADEUS database.

[43]        This assumes a systematic
relationship between the development level of a country and the net outward
investment position.

[44]          This phenomenon was
initially described by Svetlicic and Jaklic (2006), Boudier-Bensebaa (2008),
Gorynia, Nowak and Wolniak (2010), Sass, Éltető and Antalóczy (2012),
Radło and Sass (2012) Ferencikova and Ferencikova (2012), Radło
(2012) and Zemplinerová (2012).

[45]          Barba Navaretti and
Venables (2004).

[46]
         COM(2010) 2020.

[47]         COM(2010)
614.

[48]        See,
for example, Delgado/Porter/Stern (2011), DG Enterprise and Industry (2007),
and the overview in Ketels (forthcoming 2012).

[49]          For a review on the
literature on clusters and networks, see Frank Lerch and Gordon
Müller-Seitz, 2012.

[50]        The
organisations are clusters that have been identified in 32 countries.

[51]          For a profile of
this and other networks specifically mentioned in this chapter, see Ketels
(2012).

[52]          See, for example,
the Danish Agency for Science, Technology, and Innovation (2011).

[53]          See Hausmann/Rodrik
(2002).

[54]          This idea fits well
into the structure of an integrated cluster programme with dedicated tools and
services for immature clusters, mature clusters, and clusters in transition.
See NGP Excellence (2012).

[55]          One example is Hanse-Aerospace,
a network of SMEs that is part of the larger Hamburg Aerospace Cluster. See http://www.hanse-aerospace.net/home.html.

[56]          Without prejudice to any
positions on the status of Kosovo.

[57]          Croatia and most
candidate countries (Iceland, Turkey, Montenegro, the Former Yugoslav Republic
of Macedonia) are excluded from the analysis. Belarus, Andorra, Monaco, San
Marino, and the Vatican State are also not included in this chapter.

[58]        Israel and Switzerland are not
members of the EEA.

[59]          The 2004 Agadir Agreement
between Morocco, Tunisia, Egypt and Jordan aimed at establishing a free trade
area (FTA).

[60]          The share of industry in another
energy-exporting country, Norway, is also fairly high – more than 40% of GDP.
By way of comparison, on average in the EU industry accounts for less than 17%
of GDP; and in the 2004/2007 accession states it accounts for 23% of GDP.

[61]          A more comprehensive
discussion of the different ways in which the economic crisis affected
neighbouring economies can be found in European Commission (2010d, 2011 b).

[62]          The data are working-day
adjusted for production.

[63]          The data are working-day
adjusted for hours worked.

[64]          Compared to the
statistical annexes of the previous publications, the new activity
classification is used: NACE REV 2. The correspondance tables from NACE Rev. 2
– NACE Rev. 1.1 and from NACE Rev. 1.1 to NACE Rev. 2, are available on
Eurostat:    http://epp.eurostat.ec.europa.eu/portal/page/portal/nace\_rev2/introduction

Table
of content

EXECUTIVE SUMMARY.. 4

1.    The
external sector in the recession.. 16

1.1               The contraction
of output. 16

1.2               Employment and
productivity.. 22

1.3               The sectoral perspective. 24

1.4               The disruption
of trade. 31

1.5               Trends in the
external sector. Openness. 33

1.6               The boom period
and imbalances. 38

1.7               The increasing
weight of exports of services. 43

1.8               About the idea
of performance. 47

1.9               Conclusions. 52

2. The EU Industry in the Global Value Chain.. 58

2.1 The many facets of international production
integration.. 59

2.2. Changes in industries' value chains since 1995. 61

2.2.1 
International linkages and the foreign content of exports. 63

2.3. Effects of the crisis on trade and international
supply chains  71

2.3.1
Geographical evolution of trade structures during the crisis. 72

2.3.2
Decomposition of  trade by product usage. 73

2.4. Off-shoring decisions of EU manufacturing firms. 78

2.4.1
Which firms offshore?. 78

2.5. Summary and policy implications. 84

Annex 1. The World Input-Output Database (WIOD) 92

Annex 2. The European manufacturing survey.. 93

EXECUTIVE SUMMARY

The 2012 report seeks to identify opportunities to make European industries more competitive by maximising the benefits of globalisation || The 2012 edition of the European Competitiveness Report provides new empirical evidence for understanding the drivers of industrial competitiveness and the opportunities and constraints faced by European enterprises in the post-crisis recession. The focus of this year report is on maximizing the benefits of globalization. It studies: ·  the development of global value chains and their impact on the value added of exports; ·  energy efficiency as a determinant of export performance; ·  the potential of FDI flows; ·  the role of business networks; and ·  the potential of European neighbourhood policies for reaping the benefits of globalisation. These topics are important because many of the drivers of and the challenges to the recovery of industrial demand and employment are to be found outside Europe. The new industrial markets outside the EU are key to European competitiveness, particularly in the context of the recovery. More importantly, however, they are crucial for European industrial competitiveness in the long term. This is because the emerging industrialised economies are increasingly competing with Europe not only in traditional exports but also in knowledge-intensive industries. Fast-growing new industrial powers outside Europe present European firms with both challenges and opportunities. These have either not been fully studied or their implications for European industrial policies have remained ambiguous.

The single market and, especially, the expansion into markets outside the EU have made EU economies more open and more specialised. Demand from non-EU countries for EU exports is thus a powerful driver of recovery. The actual impact, however, differs from one EU country to another. Economies affected by the pre-crisis real estate bubble are undergoing painful adjustment and deleveraging. The resultant drop in internal demand cannot be fully offset by demand from outside the EU. || The report starts by putting the stalled recovery into the context of Europe's external trade performance. It argues that even though trade plays an important role in the recovery from the crisis, exports alone will not lead the EU out of the current crisis. The opportunity to rely on foreign demand can be very important in the short term when domestic demand is particularly weak but in the long term sustainable growth will be generated through technical progress and productivity growth. It is in that sense that the modernization of the industrial base and the removal of institutional impediments to entrepreneurship can be seen as crucial for the European enterprises' competitive performance in and outside Europe. The recession began when accumulated speculative bubbles in the US and certain EU Member States finally burst. These overpriced assets, and the related distortions of allocative efficiency, are typical for long periods of stability such as 1993-2007. In countries affected by the bubble (e.g. Spain and UK), the subsequent crisis is followed by a long period of slow deleveraging that explains the difficult recovery. In these countries the bursting of the bubble and the deleveraging of firms and households is a process of painful adjustment. In countries without such internal imbalances (e.g. Germany and Sweden), the contraction in GDP is almost entirely due to shrinking intra-EU exports of goods and services and to postponed investment given the uncertain business conditions of the EU. Consequently, the recovery is expected to be faster in countries in the former group as uncertainty fades away. In the future recovering exports to fast growing economies outside the EU will certainly contribute compensating for weaker domestic and EU demand in both groups of countries. The analysis of export specialization trends of EU member states also sheds light on the impact on recovery of the different patterns of export specialization. In the last two decades the EU member states increased their openness in terms of share of exports relative to GDP. For EU-15 Member States the Single market explains only part of this increase in the early 1990s. After that the share of exports to the EU remains relatively stable: the export expansion is mainly outside the Single market. This expansion is accompanied by increased specialization in exports of manufactures or services. Traditional manufactures exporters like Germany or France specialize further in this direction. Meanwhile, UK, Denmark, Greece and Ireland display a notable increase in the export of services. The study also looks at how competitiveness is fostered by the institutional and regulatory environment. It is argued that structural and institutional reforms may not offer quick-fix solutions but given the current fiscal constraints they appear plausibly as a key element of a cost-effective policy response for a way out of the crisis. In the longer term growth depends on the ability of an economy to adopt and develop new ideas. In turn, this ability depends crucially on having the right institutional and regulatory environment.

Outsourcing of production is important driver of cost optimisation and new market penetration. Hence EU industries’ positioning and performance in the global value chain, measured through their domestic content of exports becomes as important guide to policy-making as the traditional measures based on export of finished goods. The share of the domestic content of EU exports is slightly lower than that of US and Japan, but the difference reflects the higher reliance on foreign inputs of EU-12 exports. China's share in EU exports is increasing, but less rapidly than its share in US and Japan's exports. Offshoring seems to be mainly cost-driven. Upstream quality gains may provide a viable alternative to cost-driven relocation.   Pro-active industrial policy may consider FDI promotion and support for the optimal positioning of the SMEs in the global value chains, as well as better-targeted instruments to encourage investment in intangibles and in process and marketing innovations || A clue to maximizing the competitive gains from globalization is the understanding of the value chain positioning and performance of EU industries. This report studies trends in the internationalisation of production and the related challenges and opportunities for EU industrial policy. Thanks to globalisation and improved cross-border transport and technological progress, outsourcing production is now an important driver of cost optimisation and new market penetration. Different parts of firms’ production processes are now located in different parts of the world, chosen according to the comparative advantages of the locations and their sales potential. The internationalisation of industrial value chains has resulted in a sharp increase in trade in intermediate and semi-finished products. The related challenges, risks and opportunities for industrial performance have significantly changed the way firms compete. Today, their positioning in the global value chain — i.e. their value-chain performance — is becoming a more important measure of competitiveness than the traditional emphasis on export performance measured through market shares and comparative advantages. How can EU industrial policy help European firms achieve the best position in global value chains? This question is especially important for small businesses (SMEs), which – for a number of well-documented reasons – cannot easily find their way to the world markets. This report tries to inform policy-making by shedding light on how industrial value-chain competition develops, and what influences firms’ decisions to outsource. It uses a new way of measuring vertical specialisation — the import content of exports, derived from the recently-launched World Input-Output Database (WIOD) — to analyse vertical specialisation patterns. According to the findings, the import share of EU 15, Japan and the US is about 10-15 %, while for the EU 12 it is significantly higher, rising to 34% during the boom period and brought down by the crisis to 30%. The analysis of the foreign value of EU exports shows that China's role is growing. From 1995 to 2007 the share of imports from China in the EU exports expanded from below 1% to about 10% for EU 12 and from 5% to 15% for EU 15. In fact, from the mid-1990s, China's share in EU-15's exports grew faster than EU-12's share. Chinese manufacturers captured even larger shares (about 20 %) of US and Japanese exports. During the crisis, only China managed to increase its share of exports from the EU, US and Japan. Imports from China increased in all major economies during the trade slump. The chapter in question shows that China's share in European, US and Japanese exports has grown mainly at the expense of domestic suppliers. The increased use of imports, including those from China, in European exports has made EU firms more competitive on the world markets. The chapter looks at four sectors which form the backbone of the EU's industrial base: chemicals, transport equipment, electrical and optical equipment and machinery. The share of trade in parts and components in each of these sectors offers new insights into the challenges of recovery. During the trade slump, trade in parts and components declined more sharply than trade in finished goods, probably because of some multiplier effect and inventory adjustment higher up the value chain. The three sectors other than chemicals depend largely on the supply of parts and components, which grew fast in the pre-crisis years and was severely interrupted by the trade slump. This could partly explain why recovery in these sectors is so difficult and is taking so long. Finally the chapter uses survey data to analyse determinants of the decision by firms to offshore as well as their choice of destinations. It finds that, other things being equal, larger companies or those with higher revenue per employee are more likely to offshore their production. Consequently, any industrial policy that helps companies grow would also improve their positioning in the global value chain. The evidence shows that offshoring might be primarily cost-driven. First, more sophisticated products seem less likely to be offshored. Second, offshoring firms tend to spend less on R&D than non-offshoring firms, but are more likely to upgrade their products more often. This finding might mean that in-house R&D and specialisation in knowledge-intensive products is an alternative to offshoring to lower-cost locations. The report also considers whether relocation may be driven by excessive regulatory costs in the source country, but does not find empirical evidence in support of this hypothesis. The findings of this chapter are important for policy-making in three ways. First, they provide useful input for an EU policy that would allow industry to reap the benefits of the global value chain. Pursuing policies that increase openness to trade helps local companies to become part of global value chains and thus become more productive. This is important since more than two thirds of EU imports consist of intermediate products which boost EU industry competitiveness and productivity. Second, off-shoring could help European industry maximise cost/quality gains with regard to finished goods. This would require a policy mix that increases the EU's share of exports of finished goods from its trading partners, especially the fast-growing new industrial powers. Third, the chapter’s insights are important since the EU aims to maximise the domestic value of its exports. Case studies show that most of the value is created at the beginning and end of the value chain. Industrial policies should therefore look at the knowledge-creating upstream parts of the value chains and at process and marketing innovations in the downstream parts of those chains.  This goes beyond the mere increase of market shares in goods and services. It includes targeted promotion of foreign direct investment (FDI), support for the optimal positioning of SMEs in the global value chains, and new instruments to encourage investment in intangibles and in process and marketing innovations.

In addition to the domestic content of exports, the reports studies their energy content and presents new empirical evidence on how energy efficiency contributes to export competitiveness. Energy efficiency gains are seen in almost all Member States. The EU leads in reducing the domestic energy content of exports, outperforming the USA and Japan. The EU is also leading the internationalisation and cross-border flows of eco-investment and eco-innovations. Eco-innovating firms are, on the whole, more successful than conventional innovators. The report provides new empirical confirmation of the effectiveness and efficiency of the EU's sustainable industrial policy and its importance for the overall competitiveness of European firms. || The report goes deeper into the structure of the value-added of exports to examine in particular how energy efficiency contributes to external competitiveness. Energy is an important component of production costs and competitiveness. The prices of energy commodities, particularly oil, have risen sharply in the last decade. Some of the causes are structural — such as globalisation and the increasing demand from developing countries, limited fossil fuels resources and overall increasing exploration costs — and tend to lead to permanent energy price increases. The recurrent energy price hikes and volatility seen in the past were often due to cyclical factors. These included the considerable rigidity of energy demand in the short term, the failure to fully anticipate its fast growth (as evidenced by low levels of exploration investments and lack of spare capacity), or concerns related to geopolitical events. Rising energy prices and volatility directly affect businesses', production costs, their economic activity, external accounts and competitiveness. The competitive losses are greater for countries or sectors that are less energy-efficient, more specialised in energy intensive products or more energy-dependent. These include countries that depend heavily on imported fossil fuels and where low-carbon (i.e. nuclear and renewable) sources account for only a small share of the energy mix. Global competition and the cross-border integration of production chains call for improved energy efficiency and offer new business and energy-saving opportunities. As a result, energy efficiency improvements can be observed in almost all countries over the period 1995-2009. In Europe, the EU-12 economies improved significantly their initial low levels of energy efficiency and the European Union as a whole consolidated its overall lead in terms of energy efficiency. In general, over the period 1995-2009, EU countries were able to export more and at the same time significantly reduce the energy embodied per unit of exports, in particular the part of energy that is sourced domestically. The EU has a higher share of foreign-sourced energy in its total exports (34% for the EU-15 and 28% for the EU-12 in 2009) relative to Japan (33%) — a country that is also heavily dependent on imported fossil fuels. The figure for the US is much lower (around 18% in 2009). Emerging economies such as Brazil, Russia and especially China are becoming increasingly important sources of the energy embodied in exports of advanced economies. The European economies have been leading the world in reducing the domestic energy content of exports. For the EU-12 this was primarily due to a significant drop in the energy incorporated domestically in manufacturing exports. For the EU-15, the most important contribution came from the drop in the domestic energy content in service exports. This has helped mitigate the adverse effects on competitiveness and terms of trade arising from the increase in the relative price of energy. An index decomposition analysis shows that, from 1995 to 2009, manufacturing in the European Union moderately increased its gross output while at the same time keeping its energy use fairly constant thanks to continuous technical improvement. Japan, like the EU, is a world leader in energy efficiency in manufacturing but did not improve its technical efficiency over this period. Manufacturing output and technical efficiency both improved in the US, but less than in the EU. Manufacturing output increased and technical efficiency improved in almost all EU-27 Member States, but their individual performances vary significantly. The highest increases in manufacturing output were seen in the EU-12 countries and Ireland, and these were also the countries that tended to achieve the greatest improvements in technical efficiency. There was a shift towards less energy-intensive sectors in the EU-12 Member States, with only a few exceptions. Looking at how eco-innovation affects competitiveness, the report finds that EU firms introducing new products with energy-saving features tend to be more successful innovators, particularly in the case of manufacturing firms. Controlling for other determinants of innovation success in the market, these eco-innovators sell more new products than conventional innovators, and this may give them an important competitive advantage. Overall, EU firms are world leaders in the increasing cross-border ‘eco-investments’ in clean and more energy-efficient technologies and products and services.  For instance, EU firms account for almost two thirds of the FDI by multinational enterprises (MNEs) worldwide in renewable energy in the period 2007-2011. They are also global frontrunners in other eco-technologies (such as engines and turbines) used to provide environmental goods and services. However, international competition is increasing, including from MNEs based in the emerging economies. To remain competitive, EU firms need to focus on exploiting the business opportunities offered by global environmental and societal goals and challenges.

FDI inflows bridge investment gaps and lead to spillovers and technology transfer Outward FDI positions EU firms in the global value chain The EU maintains its lead in inward and outward FDI but is losing its attractiveness as an FDI destination This is mainly due to a decline intra-EU flows. Inflows from outside the EU are dominated by advanced economies (the US, Switzerland, Norway) but emerging economies are gaining relative weight. The report finds that the major drivers of inflows have been the single market, the single currency and cost advantages in the case of west-east flows. The importance of fiscal incentives is not confirmed empirically; the impact of unit labour costs and tax rates differs between countries. Since FDI can help boost the competitiveness of European firms the EU must design policies for attracting FDI and maximising its benefits. || This yearʼs report attaches primary importance to the potential of Europeʼs foreign direct investment (FDI) policy for fostering industrial competitiveness. It examines the EUʼs positioning as a source and destination of cross-border capital flows and the implications for the competitiveness of European firms. The European Union is a major player in global FDI, both inward and outward. This reflects both the potential of the Single Market and the ability of EU companies to successfully compete in EU and non-EU markets. In the most recent years, however, the EUʼs share of global inward FDI has declined significantly. The crisis meant a severe drop in intra-EU flows:  European firms were less able and less willing to invest in the EU market. Consequently, FDI from non-EU countries became more important. Companies based in developed countries, mainly the US and Switzerland continued to dominate this picture, but FDI inflows from emerging economies also gained in importance. Analysing the structure of inward FDI in the EU, relatively strong foreign presence can be observed in some manufacturing industries, such as the chemical industry and petroleum refining. EU firms are the most important direct investors in the world. However, since 2008 European multinationals have curtailed their FDI activities. In outward FDI there has been a shift from intra-EU to extra-EU flows. Low growth in the EU as a whole during the economic crisis may lead many European MNEs to seek investment opportunities in fast-growing emerging markets outside the EU.  Nevertheless, extra-EU outflows continue to be highly geared towards developed markets, particularly to the US and EFTA countries. EU MNEs seem to be more globally competitive in manufacturing industries (e.g. chemicals, machinery and vehicles) than in service industries. The overall trends in the EUʼs outward FDI mostly reflect the EU-15 pattern. However, over the last decade, there have been several signs that the EU-12 is gradually catching up. Investments by EU-12 companies is concentrated within the EU and dominated by the service sector. The crisis-induced decrease in inward FDI to the EU raises some important questions. What are the main factors influencing companiesʼ decisions about investing in the European market? How can the European market be made more attractive? A number of factors can be distinguished: · institutional factors, including the legal and administrative system and international agreements; · economic factors, such as market size or labour costs and skills; · business facilitation, such as investment promotion; · local factors at the level of individual firms The empirical analysis shows that the driving forces behind inward FDI in the EU are cost advantages, the euro and EU membership. The impact of unit labour costs and corporate taxes on bilateral FDI stocks differ from country to country. In particular, the rate of corporate taxes seems to be a key factor in the EU-12 countries, and in the case of greenfield investments in the EU-27. In addition, the analysis shows that rising unit labour costs in some EU-15 countries are a major factor in slowing the growth of inward FDI stocks, and it confirms the importance of having a well-educated workforce. In general, countries seem to benefit from hosting multinational companies. Their presence can bring in finance, technology, skills,  management techniques and good practices, and may ensure market access. The empirical analysis shows that foreign affiliates do a lot to boost productivity in EU manufacturing industries. The anaylsis shows that backward linkages (effects from foreign companies to local suppliers) are more important than horizontal spillovers for productivity growth. The empirical analysis of EU-10 countries suggests that the presence of foreign firms helps to create jobs in the local supply industries. FDI spillovers via backward are greatest for innovative local firms and especially for those that do not export. This would lead to the conclusion that foreign firms act as catalysts encouraging domestic suppliers to introduce technological innovations. The review of the home country effects of outward FDI shows that the effects on productivity in the home country are mostly positive. The empirical analyses provide a basis for some policy conclusions. It has been shown that the best way to promote internationalisation through outward FDI is not to provide subsidies and targeted support, but to promote a competitive business environment, which ensures that resources are reallocated to the best performing firms. It is also crucial to provide conditions which allow small firms and small MNEs to grow. To attract FDI into the EU it is essential to improve cost competitiveness, but a well functioning internal market and the single currency remain key factors. When it comes to promoting investment policy-makers in different Member States could usefully learn from one other about their most successful practices. The analysis of the impact of FDI suggests that industrial policies should contribute to increase spillovers from MNEs on local enterprises, in particular through networks. Also crucial for maximising the benefits of inward FDI are policies that facilitate technology transfer between MNEs and local firms and that help companies in building their capabilities.

Globalisation is also changing the way firms cooperate. Clusters and networks offer additional benefits from inter-firm spillovers. Networks enable EU SMEs to reach critical mass, share information and enlarge their industrial scope Public authorities have an interest in helping firms create networks. In practice, in-kind instruments tend to be more effective. EU networks are useful complements to existing regional and national cluster programmes. || Globalisation changes the way firms compete, but also the way they cooperate. It also shifts the pattern of their cooperation from clusters to networks. Networks not only help firms reap the benefits of FDI, as described above, but are also a good way for firms to adapt to globalisation. This report looks at non-price and non-contractual interactions that are tending to grow among independent companies, such as the formation of clusters and networks. In the case of clusters — firms carrying out similar activities in the same geographical area — the linkages arise automatically from the interplay of market forces. In the case of networks, however, it is up to the firm to establish linkages with other companies without being formally absorbed into their organisational structure. Clusters have long been an object of academic study and an instrument of industrial policy for regional and national authorities. Networks of firms, however, have been a more elusive topic — not very easy to identify and not attracting policy recommendations. But globalisation and the new organisational structures that firms are adopting in its wake have increased policy-makers' interest in networks and in their usefulness as a policy tool. The important question is to what extent networks can be used to enhance the performance of cluster-based policies and to support SMEs in the process of internationalisation. Networks spring from autonomous decisions of companies that decide it is in their best interest to be inside the network rather than outside it. Unlike clusters, networks do not need to be concentrated in a specific area. In fact, a group of companies that cooperate in a region may decide to set up closer links with other groups in more distant areas. There may be several reasons for these moves: a lack of critical mass in the original region; sharing information with other companies for the purpose of entering new markets; enlarging the firm's industrial scope. Such needs are felt more acutely by SMEs, for whom the cost of access to suitable information on international markets can be exorbitant. Faced with globalisation, SMEs have an incentive to identify emerging activities that will give them a new competitive advantage. Cooperation within a network may be a sensible strategy for preventing the decay of their traditional specialisation. In Italy, for example, the Romagna Creative District is a network focusing on communication, art, design, architecture, theatre, music and literature. It aims to connect and share the resources of individuals and companies for the purpose of achieving new creative projects and spreading them across the Romagna Region. In Germany, the Eastern Ruhr Industry Network in another example of efforts to boost competitiveness in regions undergoing industrial change. In this case, the network brings together  firms in traditional manufacturing sectors. Public authorities may share with firms an interest in building more effective and widespread networks. In this case, alongside financial incentives, regional and national governments have at their disposal ‘in-kind’ instruments such as providing structures to collaborate. Which instruments to choose depends on the activities policy-makers want to encourage. Generally speaking, the rationale for public policy intervention rests on externality or information asymmetry or on other market or regulatory failures. There is an argument for promoting clusters in terms of the positive externalities that an agglomeration of industries may well foster. The case for supporting networks is less straightforward and crucially depends on the activities that networks are engaged in. For example, accessing new markets and developing new products demand very precise information and close cooperation that could be best achieved through a common network. If there is going to be any kind of public involvement, policy-makers must show that it is more efficient to help the network than its individual members. The removal of administrative barriers and the access to a common knowledge infrastructure and collaboration platform could boost network activities in new areas that are fundamental to growth. Europe-wide network programmes could be a useful complement to cluster-based programmes.

Several large economies dominate the EU neighbourhood in terms of population and GDP Most economies suffer from lack of competitiveness… Asymmetry in partnership Opportunities of export-led growth largely missed EU is the most  important investor in the neighbourhood Inward labour migration is an opportunity rather than a challenge for EU growth and competitiveness || Finally the report looks at the potential of neighbourhood policies to contribute to growth and industrial competitiveness. The opportunities of cross-border investment and trade with our neighbours are in a way the low-hanging fruits that have not yet been used to their full potential. The importance of each neighbouring country for the competitiveness of the EU and its Member States varies depending on the form of cooperation between the EU and the country in question, how deep and comprehensive the cooperation is, the size and structure of the economy of the neighbouring country, its level of development, trade and investment flows, any bilateral agreements, and migration between the country concerned and the EU. By examining each of these aspects, the chapter endeavours to shed light on the challenges and opportunities for EU competitiveness stemming from its neighbourhood in the context of globalisation, also reflecting the dynamics over time in terms of EU enlargement, the global economic crisis, evolving relations across borders, and internal developments in neighbouring states (such as the Arab Spring). A few large economies dominate the neighbourhood: Russia, Ukraine, Switzerland, Norway, and Egypt. Without these countries, the region surrounding the EU would be significantly less important in terms of GDP and have less than half its current population. Oil and gas  production plays a central role in a small number of countries – Russia, Algeria, Azerbaijan, Libya, Norway – while most countries are service-based economies, in many cases also with a relatively large agricultural sector. Most countries in the neighbourhood suffer from a lack of competitiveness, in many cases as a result of being relatively closed economies with weak business environments. Many of them also run high external imbalances – usually deficits, apart from the energy exporters listed above which all have persistent trade and current account surpluses. The EU is an important trading partner for all neighbouring countries. From the point of view of the EU though, they play rather a modest role as trading partners, for the reasons explained above. This asymmetry in the relative importance of trading partners has an impact in bilateral negotiations as any development affecting trade relations is likely to have much more impact on the non-EU trading partner than on the EU. The type of extensive and successful export-led growth strategy witnessed in recent decades in other parts of the world, with the potential to diversify and upgrade exports and integrate economies into global trade networks, has so far had less success in the countries surrounding the EU. Most of them have not seen their market shares increase on the world market, most likely due to their relatively small shares of manufactured goods in their exports. In addition, several of the neighbouring countries are caught in a situation where rents from natural resources prove detrimental to export diversification and structural upgrading. Outward FDI from the EU to its neighbours exceeds inward FDI from the neighbours. Around a fifth of all outward extra-EU FDI from Member States goes to the surrounding region, with the exception of 2009 and 2010 when the share was much higher. In the opposite direction, more or less a quarter of all inward FDI comes from the surrounding region, a share which however has dropped recently. The Southern Mediterranean is an important destination for EU investments, in particular Egypt, Tunisia and Morocco. While in Egypt most FDI has gone into the petroleum industry, FDI flows into Morocco have been more diversified. Mainly for historical reasons and due to its geographical proximity, the EU is in fact the leading investor in the region. Labour migration to EU Member States is high on the agenda of EU policymakers. Mediterranean neighbouring countries are a major source of EU immigration, the total number of first-generation emigrants from that region ranging from 10 million to 13 million, as for various reasons the EU is the main destination for migrants from the other side of the Mediterranean. Immigrants from the region represent 20 % of the 30 million immigrants in the EU and 6 % of total EU population. The flow of migrants from the region could rise, at least temporarily, against the backdrop of the Arab Spring. Migration is obviously linked to local unemployment, economic hardship and a lack of options. It can represent the only viable alternative to unemployment, and is a natural reaction to social and economic upheaval or internal political conflicts. Faced with the prospect of ageing and potentially diminishing populations exerting serious pressure on their welfare systems and potentially holding back their competitiveness, EU Member States have come to see immigration, not only from the immediate neighbourhood but from further afield as well, as a solution. The Europe 2020 strategy set out to promote a forward looking and comprehensive labour migration policy which would respond in a flexible way to the priorities and needs of labour markets. By matching shortages on EU labour markets with the excess labour supply outside the EU, Member States could sustain their international economic competitiveness, growth and prosperity. Remittances go hand in hand with labour migration. Both have increased over the last decades, in many cases generating significant welfare gains in the countries to which remittances are sent. Moldova is an extreme case in point as it has the highest share of remittances to GDP (23 %), and remittances contribute to developments on the labour market there. Other countries with high shares of remittances to GDP are Lebanon and Egypt. However, the economic crisis and ensuing austerity packages implemented in many Member States have made it more difficult for immigrants to find gainful employment in the EU, and while some of them have returned to their countries of origin, most immigrants have adjusted to the economic crisis by reducing their remittances. The report is structured as follows. The introductory chapter "The External Sector in the Recession" sets the scene by studying the role of the external sector in the European industries' recovery and their sustainable competitiveness. Chapter 2 "EU Industry in the Global Value Chain" studies the internationalisation of production and the trends in the domestic value of European exports. Chapter 3 "Energy Content of Exports and Eco-Innovation" analyses competitiveness in the context of energy efficiency of exports. Chapter 4"FDI Flows and EU industrial competitiveness" examines the positioning of the EU as a source and destination of cross-border capital flows and the related implications for the competitiveness of European enterprises. Chapter 5 "Clusters and Networks" studies the changes in the way firms cooperate and the room for policy support. The concluding chapter 6 "Competitiveness developments along the external borders of the EU" looks at the potential of neighbourhood policies to contribute to growth and competitiveness.

1.      The external sector in the recession

The
EU is experiencing a large and long recession, both in depth and scope. The
recession was preceded by a long period, from the mid-1990s to 2007,
characterized by macroeconomic stability and sustained growth. Indeed, as in
previous large recessions combined with a banking crisis, ‘[t]he crisis was
preceded by a long period of rapid credit growth, low risk premiums, abundant
availability of liquidity, strong leveraging, soaring asset prices and the
development of bubbles in the real estate sector’. [1] Within
the EU, some Member States became net lenders by a significant fraction of its
GDP while other became large net borrowers. These developments distorted the
financial position of many European countries feeding what today is referred to
as external imbalances.[2]

This
chapter is an overview of the consequences of the crisis with a particular
emphasis on the external sector. When examining the performance of exports and
imports, it tries to elucidate to what extent what it is observed, the external
position of EU members, reflects a true gain or loss of competitiveness or is
simply a reflection of the internal imbalances accumulated during the boom
years, and in so doing highlights the challenges faced by EU economies.

              1.1     The contraction of output

The
current crisis is unprecedented in that it is deep and it has affected many
economies around the world, particularly the US and the EU. Although the causes
of the current global economic crisis are complex, the origins can be linked to
growing mispriced assets, notably real estate, both in the US and some EU Member States. The recession was triggered by increasing doubts of the sustainability
of these prices in the US, undermining the soundness of mortgage-backed assets
and ultimately dragging the US financial sector into serious disruption towards
the end of 2007. The disruption in the financial sector announced a sharp
recession in the US in 2008 which hit global demand. In addition, the
internationalisation of financial products linked to US real estate lending
meant that the fall in the US real estate market affected financial sectors
globally. Trouble in the US pricked the bubble in some EU countries leading to
a serious recession on this side of the Atlantic. Between 2008 and 2009 the EU
suffered a large contraction of economic activity: more than 5% of GDP with
respect to the peak value for the Union as a whole, whereas and in some Member
States the drop in GDP was well beyond this figure.

Figure 1.1. The contraction of GDP
in 2007-09 across Member States

Source: Eurostat,
Annual National Accounts.

The
recession is not only deep, it is also prolonged. Table 1.1 illustrates
the duration of the recession. Some EU Member States like Greece have been in recession for more than two years in a row. Not all EU Member States have been equally
affected. Figure
1.1
and Table
1.1
show how heterogeneous the experience has been across Member States: from Poland, virtually unaffected by the crisis, to the Baltic Republics, with cuts in activity reaching
25% and several consecutive quarters in recession.

Table 1.1. An overview
of the recession: Real GDP in 2007-11; index, 2000=100

Notes: Numbers are indexes relative to 2000 so that it can
be appreciated how much the series has grown in the boom years, and compare it
with the extent of the contraction. The shaded cells denote a decrease in value
vis-à-vis the previous quarter.

Source: Eurostat,
Quarterly National Accounts and own calculations.

Table
1.1
also illustrates how many European economies are slipping into a second
recession, this time due to the uncertainty surrounding the EU sovereign debt
crisis which has weakened demand, along with the phasing out of fiscal stimulus
measures in some EU countries and the US. Indeed, apart from countries that
entered the recession with serious structural public deficits, notably Greece,
in some Member States the low revenues caused by the sluggish economic activity
add to the troubles of the financial system ¾notably its exposure to
the real estate market¾ triggering a fresh sovereign debt crisis[3],
which is likely to be at the origin of the slowdown or even the reversal of the
recovery.

Figure
1.2
illustrates this reversal. Most EU countries grew for several quarters in a row
in 2010 but in the course of 2011 it became obvious that an increasing number
of them were experiencing again a contraction on a quarter-to-quarter basis. By
the last quarter of the year 15 Member States reported a decrease in activity
with respect to the previous quarter. In this respect, although the main
stimulus measures in 2009-10 undoubtedly cushioned the negative impact of the
crisis and supported growth along with the relaxation of monetary policy, EU
economies have struggled to gain momentum as the stimulus measures were withdrawn.

Figure 1.2. Number of
countries with decreasing GDP vis-à-vis the previous quarter

Source: Eurostat,
Quarterly National Accounts.

EU
Member States have been affected in a different way both in terms of the
initial contraction and the subsequent (weak) recovery. Within the EU large
capital flows accumulated substantial imbalances by the end of the boom period.
As a consequence, at the end of this period the international financial
position of some Member States was seriously distorted, either becoming large
debtors or creditors. On this basis countries can be classified basically in
four groups.[4]
In the first group we find traditional net lenders, like Belgium or the Netherlands. In a second group we have Germany or Sweden that started the boom period
being borrowers and became large net lenders. Countries in this group became
net lenders because others, the third group, became large net borrowers. Within
the former, however, we find different underlying reasons to become net
borrowers. For example, in the case of Greece at the origin of its borrowing we
find large and persistent public deficits financed with public debt mostly
placed outside Greece, mostly to financial institutions in France or Germany. In the case of Spain or the UK the driving force were mispriced domestic assets,
in particular houses, so it is private institutions leverage (banks and
households) what we find behind the aggregate net borrowing. Some EU-12 Member
States like the Baltic Republics suffered from bubbles probably associated with
the large inflow of capital, otherwise typical of the rapid catch-up process in
which they are immersed (see Figure 1.3); in these cases the
causality is probably the reverse: the capital inflows generated the mispriced
assets rather than the other way around. Finally, Portugal and Italy show a remarkably weak growth performance, mostly because of low productivity growth
(see Table
1.3
below).

Figure 1.3. The
catch-up process of the EU-12 countries 1994-2007. Changes in relative income (EU-27=100)
and initial level of income

Note: Income
is expressed relative to the EU-27=100. A negative value means that the country
has lost income relative to the average. In other words, it denotes a growth
rate below the average growth rate.

Source: AMECO database
and own calculations.

Each
of these groups was affected differently during the initial recession, and has different
pattern and drivers of recovery. There is one aspect, however, in which most
countries behave similarly: exports are recovering strongly for most countries,
probably reflecting an independence of internal developments and the healthy
condition of many non-EU economies. In countries affected by serious internal
bubbles, the recession can be seen as a correction to come back to more realistic
asset prices. In these economies, private agents like households and banks, are
immersed in a deleverage process that is by definition slow and tough. Indeed,
the excess investment in mispriced assets (e.g. houses), whose prices are only
sluggishly returning to normal lower levels[5], has left many agents
highly indebted with less assets to back their debt (e.g. a large mortgage for
a house that is not worth the mortgage).

This
argument can be illustrated comparing the UK, a net significant borrower, and Sweden, a net lender. Figure
1.4
shows how at the onset of the recession GDP reacted similarly in both
countries. Underlying, however, were quite different reactions of the different
components of aggregate demand. In both countries investment reacted similarly
to the uncertain business conditions. However, the main driver in the Swedish
recession was the external sector and uncertain business conditions as
reflected by the drop in investment: in five quarters both investment and
exports had contracted by 20%. In the case of the UK it was households'
consumption that dragged down income: compared to a mild and brief contraction
in Sweden, UK private consumption contracted more than double and has not
recovered yet.[6]

There
is one aspect that most EU Member States have in common with Sweden and the UK: the relatively strong recovery of exports. A glance at Table 1.7 in the
appendix shows a heterogeneous behaviour across countries when comparing
exports and income. This is a recall that the external sector can soften the
impact of a recession and contribute to a recovery but cannot fully compensate
for other internal factors that ultimately must lead the recovery. In
particular, it is unlikely that a weak internal demand can be compensated by
external demand in medium to large countries.

Figure 1.4. The
recession: A comparison of Sweden (blue) and the UK (red); indexes, 2008Q1=100

Note: Exports
include goods and services

Source: Eurostat,
Quarterly National Accounts.

              1.2     Employment and productivity

The
evolution of employment and unemployment reflects the way the crisis is shared
among all actors in the economy. In Table 1.2 we can see that at the
EU level employment, compared to some Member States, has remained remarkably
stable, with a contraction of 3% between mid-2008 and the end of 2010.[7] But this
aggregate relative stability masks considerable heterogeneity at the Member State level. For instance, in countries such as Belgium or Germany the crisis has hardly affected the level of employment whereas in countries such as Spain employment was still contracting going into 2012, down 14% on the peak value in the
last quarter of 2007.

Institutional
differences and the accumulation (or not) of internal and external imbalances are
key to understanding the labour market performance across Member States. In
particular, Member States affected by an oversized construction sector are
among those most affected by large contractions of employment (see Figure 1.7 below) and
large increases in unemployment. The reason is that in these countries the
construction sector has to be downsized so the changes in employment are
permanent – labour hoarding only makes sense to preserve firm-specific human
capital when the downturn is perceived to be temporary.

Table 1.2. An overview
of the recession: Employment in 2007-11; index, 2000Q2=100

Notes: The numbers
are indexes relative to 2000 illustrating the degree of growth in the boom
years, and to compare it with the amplitude of the contraction. The shaded
cells denote a decrease in value vis-à-vis the previous quarter.

Source: Eurostat,
Labour Force Survey (LFS) quarterly data.

From
the institutional point of view, differences can also be linked to distortions
induced by labour market regulations. For instance, unemployment rose much less
steeply in the US than in the EU Members States badly hit by the crisis, where
labour regulations are more stringent and tend to result in wage rigidities in
a way or another. And it is not only the degree of stringency but also the
distorting nature of certain institutions. For instance, within the EU, the
Spanish labour market stands out for its dual nature, with overprotected stable
contracts on one side and workers on fragile temporary contracts on the other
side. This explains the overreaction of unemployment because adjustment tends
to be in terms of employment (reduction of temporary workers) rather than wages
(influenced by the stable workers).[8]

On
the positive side, as this is a demand-driven recession, it is likely that
after the recovery, in the medium to long term, the labour market will recover
its trend previous to the crisis (see Table 1.3). Currently some Member
States are undergoing a large restructuring to bring down some oversized
sectors, notably the construction sector. But large structural (sectoral)
readjustments in the longer-term are not likely to follow unlike what happened
in the 1980's when entire industrial sectors, notably heavy industries,
underwent a severe restructuring. The exception to this rule is probably
Ireland and Spain where the bubble grew out of attracting a considerable number
of foreign workers (see table 1.3) and increasing notably the activity rate. In
these countries the labour market is likely to slow down for some years to
come.

Table 1.3. Real GDP, productivity,
and components, changes 1998-2007

Note: Changes
in real GDP per head are decomposed in two ways. The first is to disentangle
changes in GDP from changes in population. The second decomposition examines
the individual effect of changes in productivity, the number of hours, the
employment rate and the activity rate.

Source: AMECO
database and own calculations.

              1.3     The sectoral perspective

In
the short-run, however, some industries, notably those producing consumer
durables and equipment goods, are bond to suffer still a long period of weak
demand. Indeed, the sectoral dimension of the crisis does not reveal
exceptional patterns with the exception of the construction sector in countries
affected by a real estate bubble. Indeed, if in absolute terms this crisis is
exceptional for its size, in relative terms the pattern of the downturn across
sectors is the usual one in which durable consumption and equipment goods have
suffered the largest contractions in activity. On their side, services and non-durable
consumption goods have been less affected, both in terms of value added and
employment, because there are smaller items (relative to the household's
budget) and basic needs that cannot be postponed as durable goods can be. This
pattern is reflected in Figure 1.7 where it is clear that
industry, and in particular manufacturing, is bearing a disproportionate share
of the burden of the crisis across all EU Member States. [9]

As
mentioned, the one remarkable supply-side feature of this crisis is the oversizing
of the construction sectors in countries affected by a real estate bubble. Table 1.5 shows that
in the boom period 2000-08 construction was almost the only economic sector
that experienced substantial growth, and it did so in those countries that were
most affected by the bubble. The only exception is Ireland and Denmark. In the case of Denmark, the difficulty to attract workers limited the growth of
the sector.[10]

Table 1.4. The sectoral structure of European
economies, share of value added in GVA, 2008

Note: The
shading emphasizes sectors with higher weight in overall economic activity
within the country.

Source: Eurostat,
National Accounts aggregates and employment by branch (NACE Rev.2).

Table 1.5. Changes in
the sectoral structure of European economies, changes in share of value added
in GVA, 2000-08

Note: Figures
are the difference in the share of the sector in gross value added between 2008
and 2000. The shading emphasizes sectors with larges changes, either shrinking
(red) or expanding (blue) relative to other sectors within the country.

Source: Eurostat,
National Accounts aggregates and employment by branch (NACE Rev.2).

These
patterns are obvious at the EU-27 level (Figure 1.5). During the
crisis it is industry, and in particular manufacturing, that has taken the
brunt of the contraction, although presumably to recover afterwards.
Construction, on the contrary, is undergoing a severe adjustment process in
some Member States so that its contraction will probably be more persistent.
The disruption of economic activity and, in particular, of manufacturing, has
an obvious impact not only on trade and transport but also on professional
services, much of whose output goes into the industry.

Figure 1.5. The
sectoral profile of the contraction in the EU-27: Real value added per sector;
index, 2008Q1=100

Source: Eurostat,
Quarterly National Accounts by 10 branches.

Finally,
the double-dip pattern shown in Table 1.1 above at the aggregate
is also reflected at the sectoral level. Figure 1.6 shows the
number of sectors that report at any given month a contraction with respect to
the previous month. By the beginning of 2012 the index was -40% meaning
that only 30% of sectors reported an increase in activity while 70% were
contracting (and hence 30 - 70 = -40).

Figure 1.6. A
qualitative-quantitative assessment of the relapse. The diffusion index

Note: The diffusion index is defined as the difference between
the percentage of manufacturing industries that

are expanding and of those that are declining. The index ranges
from -100 to 100. ‘Expanding’ and ‘declining’ mean positive and negative growth
rates respectively. The total number of industries used in the calculations is
93 (defined in terms of the 3-digit level of NACE Rev. 2). For more details see
the European Union Industrial Structure 2011.

Source: Short-term
Industrial Outlook, April 2012, DG Enterprise and Industry, European Commission.

Figure 1.7. Changes in
employment per Member State by economic activity, percentage change 2008-11\*

\* Data for
2011 not available for three countries: UK uses 2009 while Ireland and France use 2010.

Note: Each
category corresponds to the NACE rev. 1.1 sections: Agriculture, A and B;
Industry, C, D and E; Construction, F; Services, from G to P; Manufacturing, D.

Source: AMECO
database, Commission services.

              1.4     The disruption of trade

This
crisis has been described as unprecedented because of its simultaneous depth
and scope. In turn, the scope is reflecting an increasingly interconnected
world. Below it is shown that European economies are particularly open and
integrated.

Figure 1.8. Openness and the disruption of trade
by the crisis, 2008-09

Note: The
disruption of trade index is the reduction in the share of imports in aggregate
demand m with positive sign and corrected by the corresponding
contraction of GDP y, that is, –[(m' – m) –(y' – y)].
Openness is exports as a percentage of GDP.

Source: AMECO
database and own calculations.

In
this recession many EU Member States were not directly affected by internal
imbalances.[11]
These countries were affected by two transmission mechanisms. One is exposure
to private or public debt in troubled economies. The other is trade linkages
and the corresponding uncertainty about business conditions that spreads across
borders because our interconnectedness. Figure 1.9 relates the
initial drop in consumption with the drop in exports at the onset of the
crisis. Countries far away from the vertical axis like Denmark, Spain, Romania or the UK are countries with internal imbalances where consumption dropped
simultaneously to exports and investment. Countries close to the axis like Germany, Sweden or France can be interpreted to be affected only indirectly through trade linkages
and general uncertainty to the first group of countries and the overall
uncertainty about business conditions.[12]

Figure 1.9. The initial
drop in consumption compared to the drop in exports, 2008-09

Note: The Baltic States are not represented in the chart for the sake of readability; their figures are
beyond the lower limits of both axes.

Source: AMECO
database, Commission services.

Openness
is an important part of the explanation of the diffusion of the crisis.
However, it could also become a component of the recovery. EU countries not
affected by internal imbalances may act as a locomotive for growth in the rest
of the UE at least in the short-term. Strong growth in other regions of the
world in particular emerging economies in Asia and South America, which are
growing more rapidly and have been much less affected by the crisis, may as
well boost external demand for EU countries, depending on their trade
orientation. That may explain the positive evolution of exports in 2010-11,
strongly growing in all EU Member States with the sole exception of Greece and Finland.[13]
However, this effect is not sufficient to compensate for the unfavourable
evolution of domestic demand. Therefore while exports are indeed recovering
swiftly and vigorously, income recovery remains elusive in many Member States.

Box 1.1. External demand, long-term growth and competitiveness In times of recession, when internal demand is weak, it makes all sense to rely on external demand to accelerate the recovery. Indeed, there is some consensus in the economics profession that short-term increases in aggregate demand ¾ including increases in external demand, the demand for exports of an economy ¾ can increase the domestic product in the short-term even beyond the obvious increase in income due to increasing sales abroad. Indeed, via some chain or multiplier effect, the increase in income may be even larger than the demand stimulus.[14] In that sense, strong growth in other regions can be excellent news for mature economies in the short term and for export-led catching-up economies in the medium term. In the long-term, however, and for advanced economies without natural resource endowments, only technical change can sustain growth of income per head. From this longer-term perspective, the connection between trade and growth has less to do with the mere exchange of goods and services and more with competitive pressures as well as the exchange of ideas that comes along with trade. Empirical evidence is elusive but points in that direction: openness increases the exposure to foreign technology, equipment goods, management techniques, and so on. Competitive pressures provide the incentives to adopt these technologies and help the market select the most productive firms.[15] Openness often comes hand in hand with mobility of persons: engineers visiting providers, students completing their curricula abroad, migrants that leave and eventually return with new ideas.[16] If the institutional setting is the right one,[17] technologies are adopted, new businesses are started that introduce new processes and commodities, and so on. This distinction between the short and the long term is important. External demand can help recover in the short-term when internal demand is comparatively weak. In the long-term, however, through openness and structural reforms that change the ability and incentives to adopt and develop new technologies.

              1.5     Trends in the external sector. Openness

The
external sector in Europe is characterized by a notable degree of openness. As
measured by the value of exports relative to GDP, the EU is considerable more
open than the other two economies with which it compares: the US and Japan.

Figure 1.10. Exports of goods and services
(including intra-EU trade) as a percentage of GDP, 2008

Note: The
criteria to classify countries is by population. Luxembourg (175%) excluded for
the sake of readability of the chart.

Source: AMECO
database, Commission services.

In
this already open landscape, four countries stand out. Among the medium- and
small-sized countries of the EU-15, Belgium, the Netherlands and Ireland are very open economies. In the case of Belgium and Netherlands, historical reasons
as well as a small size and a geographical location may explain much of this
openness. The case of Ireland, despite its peripheral location, can be
explained again on its small size and on recent trends that have to do with the
English language and a tax regime favourable to the establishment of many
foreign services and manufacturing corporations for their operations in Europe. The take-off of Ireland as a hub for many multinational corporations is likely
explained by these reforms and, in any case, is reflected in an already large
48% in 1983 to an outstanding 80% before the crisis in 2008.

The
fourth country in question is Germany and constitutes a notable case. Among the
big countries it has a degree of international integration which is quite high.
As Figure
1.11
and Figure
1.12
show, this is a relatively recent phenomenon that took-off in the early 1990s.
But the underlying drivers of these changes are not clear. Below the case of Germany is examined in some depth.

Figure 1.11. Exports of
goods and services as a percentage GDP, recent evolution, selected countries

Source: AMECO
database, Commission services.

Most
EU Member States display an increasing trend in the value of exports relative
to GDP due to the increasing globalization of EU economies as well as European
economic integration itself. After the impulse of the Single European Act, this
is mostly reflecting increasing integration in world markets.[18]

But
this trend has been particularly pronounced in four countries within EU-15
Member States. Belgium and the Netherlands have been already signalled as
particular cases. Sweden, on its side, is probably regaining its place in the
international scene after a period of poor performance during and after the
crisis of the 1970s. The case of Germany, however, is less easy to explain and
is the only one that affects a large country; the largest economy of the EU
indeed. As illustrated in Figure 1.12, larger countries have smaller external
sectors (as a percentage of GDP) because more trade occurs within its borders.[19]
For example, and to support the assertion above, Sweden has now the degree of
openness expected for a country of its size.

Germany, on the
contrary, was on the average in 1995 (see again figure 1.12) with total exports
being 24% of GDP. Yet, in 2007 and given its size it should still be around 25,
and nonetheless its exports represent currently up to 47% of GDP.

Figure 1.12. Changes
1995-2007 in openness relative to the size of the economy

Source: AMECO
database, Commission services.

One
possible explanation lies in the internationalization of the value chain. As a
large manufacturer, Germany has close ties with some of its neighbours such as the
Czech Republic, Slovakia and Hungary. However, evidence remains elusive: trade
in intermediate goods, commodities used to produce other commodities, has not
grown faster than general trade. The share of exports of intermediate goods to total
exports has remained remarkably stable over this period (Table 1.6).[20]
It grows in absolute terms hand in hand with the general level of openness. The
so-called internationalization of the value chain seems to be an absolute, not
a relative, phenomenon.

Table 1.6. Share of exports of intermediate
goods to total exports

Source: OECD STAN
Bilateral Trade Database.

Figure 1.13. The international of value chains:
Openness and exports and imports of intermediate goods

Source: OECD STAN
Bilateral Trade database and AMECO database, Commission services.

Figure
1.14 suggests that through trade the country is strongly specializing in
manufactures but no single trade partner explains this trend. For example, China or Poland has become important markets for Germany but are not yet comparable to France, the US, or Italy.[21]
The figure
shows
how exports have grown similarly for all trade partners with no overwhelming
importance of any individual partner. All in all it seems that further research
is needed to understand the increasing internationalization of the German
economy.

Figure 1.14. German
exports in current prices, main trade partners

Source: OECD STAN
Bilateral Trade database.

              1.6     The boom period and imbalances

The
trends mentioned above do not seem to have been altered significantly by the
events that preceded the recession. Mispriced assets have the potential to
distort the real economy, for instance diverting capital to mispriced property
or stocks instead of productive investments. In that sense, the risk is that the
imbalances not only feed the current recession but also hamper future
productivity growth because of this inefficient allocation of capital.

Somewhat
paradoxically, however, the speculative bubbles that grew over the boom period
created considerable financial capital flows but have not affected seriously
the real economy as measured by productive investment or external performance.[22]
Not even within the euro area, despite is fixed exchange rate, can we observe
significant distortions. Figure 1.15 depicts the percentage
of trade destined to the EU market. Most countries fluctuate around their
historical trends: Belgium around 70%; the UK from almost nothing in 1960
stabilized around 45% in the 1980s; after accession, Spain reached 63%; or
Sweden that was remarkably stable around 45% before and after accession.
Otherwise, these series have remained quite stable in the last 20 years. If
anything, we observe a slight decreasing trend for some countries like Spain, Belgium and the UK.[23]

Figure 1.15. The share
exports of goods to the EU over total exports, selected countries

Source: AMECO
database, Commission services.

If
the boom years did not reveal any obvious impact of the accumulated imbalances,
the subsequent recession and the current sovereign debt crisis do not seem to
have had impact on external performance as measured by the share of exports in
world exports.[24]
 Figure
1.16
represents the international market share for the economies in trouble with Germany as a comparison. There is a decreasing trend most likely due to a composition
effect because of increasing globalization.[25] Some other long-term
trends are also apparent: Italy and the UK are losing market share relatively
faster than other EU countries, or the Spanish share remaining remarkably
constant along this period. Other than that, the build-up of the imbalances and
the burst of the bubble do not seem to have harmed the ability of these
countries to export.

Figure 1.16. Export
market shares, selected countries

Note: Share
of exports of goods including intra-EU trade over total exports. This excludes
services; in the light of section 1.4 above, it is important to keep this in
mind to interpret correctly the series of the UK, IE and EL.

Source: AMECO
database, Commission services.

Box 1.2. Competitiveness and public finances: The case of Greece Despite current turmoil, Greece performed reasonably well in the years preceding the crisis. After a period of relative depression in the 1980s, the country took-off in 1993 for a long period of sustained growth. During the boom years Greece had improved by 40% its relative position in the distribution of income in the EU. That was reflecting true improvements in standards of living: since the take-off, and before the crisis, Greek GDP per head in purchasing power standards had closed significantly the gap with the EU average, and had reached similar levels to Italy by 2007.[26] At the same time, the external performance of the country was relatively stable in goods (see Figure 1.16 above) while section 1.9 discusses the notable performance of the export of services.[27] Real income growth. Comparison with selected EU-15 Member States Source: Penn World Table 7.0, CIC, University of Pennsylvania. Hence, it seems that the ongoing trouble in Greece is less related to a lack of external competitiveness and more with a government solvency problem that spilled over the real economy through uncertain business conditions and stringent programs to close the gap between revenues and expenditures. At the beginning of the expansion period, growth came along with an increase in government revenues almost closing the gap with expenditures in a decade. Then, in 2000, the trend is reversed and despite ongoing growth of income government revenues as a percentage of GDP start to lag significantly below expenditures that remained constant. With the exception of Hungary, no other EU Member State runs so large public deficits in the booming years immediately before the recession. Public revenues and expenditures in Greece Source: AMECO database, Commission services. It seems, then that the Greek problem is more related to the ability of the government to raise revenues rather than the ability of its industry to exports goods and services. [28]  Alas, if the accumulation of public debt did not seem to affect the real economy, it does not seem that the same is true for the uncertainty surrounding the resolution of the crisis as well as the drastic measures that try to bring public expenditures and revenues closer. In Table 1.1 Greece appears as the only country that has been in recession since the onset of the crisis. As for the future, while the country has been successfully growing in these past two decades, catch-up is still partial. If the economy seems to keep up the pace of development of the EU, and even improve its relative position, in many respects Greece is still well below the EU average. Indeed, despite progress, Greece could improve sensibly along a number of dimensions (income per head, labour market participation, etc.). Most notably, it is still a much closed economy: for its size, exports relative to GDP ought to be around 50% but they represent hardly 25% (see Figure 1.12 above). In the sections below it is shown that Greece is at the bottom of the class when it comes to business environment as measured by the Doing Business indicators. Improvements in these areas would certainly help the country leap ahead.

              1.7     The increasing
weight of exports of services

Together
with increasing openness, a sign of these last decades is the growing
importance of services in international trade: financial services, civil
construction, transport, environmental services, and so on.[29]

Exports
of services constitute an important share of total exports for the EU and as a
whole, close to 25% for the EU-15 in 2008 after a long period of moderate but
constant increase. Together with the US, with services weighting 29% of total
exports, the EU is one of the most important providers of services in the
world. The aggregate figure, however, masks considerable heterogeneity within
the EU. Several groups can be distinguished.

Countries
like Germany, France, or Italy are traditional exporters of manufactures. The
service sector contributes relatively little to exports. The fast catch-up
process of Slovakia, the Czech Republic or Hungary is mostly based on FDI
inflows that explain important increases in exports of manufactures. From these
countries most exports are goods rather than services. Countries like UK, Greece, Ireland, Denmark and Malta stand out for the large weight of services in their exports.
Furthermore, these countries have shown an important increase in the last
years. For instance, in Greece it has moved from an already high 35% in 1995 to
close to 55% in 2008. The ultimate explanation for these changes differs across
countries. The UK is the largest economy of the EU where services have grown to
be so important, and a glance at Table 1.5 makes obvious that it
is closely linked to the expansion of the financial sector: between 2000 and
2008 Financial and Insurance activities have gained almost 4 percentage points
of weight in gross value added, a change that reflects the size of a sector
that today represents close to 10% of GDP, the highest share in the EU together
with Ireland. The case of Greece, instead, is linked to the transport sector,
most likely because of the traditional importance of the cabotage industry.

Figure 1.17. The weight
of exports of services in total exports; comparison 1995-2008

Source: AMECO
database, Commission services.

It
may be worth noting that these notable increases in shares reflect real growth
of exports of services rather than shrinking exports of goods. These four
services' exporters have experience large real increases of exports of
services, in the case of Ireland reaching a ten-fold increase in since 1991
(see Figure
1.17).
This contrasts with more manufacturing-oriented exporters like Germany or France where the share of services in exports is moderate, between 15% and 25%, and has
remained stable. In these countries the real evolution of services lags
moderately the real increase of merchandise exports, maybe reflecting poor
domestic performance in services.

Figure 1.18. Some services' exporters. Real
growth of exports of goods and services; index, 1991=100

Source: AMECO
database, Commission services.

At
the aggregate EU level, the importance of services' exports has increased
moderately from 20 to 25% between 1991 and 2011 but it is still relatively
lower than the US and definitively higher than Japan, a classical exporter of
manufactures. In real terms, aggregate EU changes are aligned with those of Japan and the US with exports of goods growing at a similar pace to services, an indication that the
patterns described above do not reflect a general pattern but rather the
relative specialization of these countries as service providers.

Figure 1.19. Real growth of exports of goods and
services in 1995-2008; index 1995=100

Source: AMECO
database, Commission services.

Finally, in the current circumstances it is
legitimate to ask whether it is goods or services that are more resilient along
a recession. The answer is that it depends on the services. In Figure 1.20 one can see
that there is no clear association across Member States. The UK or Denmark, more focused on financial services, exports of goods have contracted more than trade
in services. In Greece, on the contrary, services have contracted more, most
likely because of the reliance on cabotage and the contraction in international
trade (and hence in international transport services). In other countries, the
weight of business services links more tightly manufacturing with services.

Figure 1.20. The
contraction of exports: Real percentage change of exports of goods and services
in 2008-09

Source: AMECO
database, Commission services.

              1.8     About the idea of performance

Having
examined recent trends and developments of the external sectors begs the
question of whether a good external performance is good per se or the
reflection of a buoyant economy capable to produce commodities demanded in the
international markets. Taking the increase in income per head as a performance
index, the correlation with the variation in export openness is positive but
weak in the medium term.[30]
This is most likely due to factors other than exports contributing to growth
other than exports. This is shown by the high dispersion of the observations in
Figure 1.21.

Figure 1.21. Exports and income growth

Note: The
change in the weight of exports is the comparison of the average 1995-98 and
2004-08, in % points GDP. The change in the share of exports in world exports
compares the average 1993-96 and 2005-08 and is adjusted by initial level of
income in euros to compensate the fact that, mechanically, in countries growing
fast, exports tend to grow fast as well.

Source: AMECO
database, Commission services.

Indeed, net
exports have an obvious immediate contribution to income in the short term.
Hence, as mentioned above, a good net export "performance" will
soften the impact the recession. In the longer term, however, even if it is
clear that trade, or more generally openness, is essential for growth and
development, the relationship is less direct than it is often assumed. As an
exchange of goods and services it has a direct welfare effect: it allows
consumers to access to a larger variety of commodities. This is, after all, the
main reason why we export: to afford imports. In the long-run, however, as
discussed in Box
1.1,
it is not trade in the narrow sense of exchange (exports for imports) but
openness in general (including foreign investment and investment abroad,
migrants, exchanges of students, tourism, etc.) that exposes an economy to
foreign technology, equipment goods, management techniques, and so on. Openness
helps technologies to circulate and provide the incentives to be adopted.
Indeed, technologies are adopted and further developed because competitive
pressures of foreign firms (both in the domestic and foreign market) provide
the incentive to local firms to improve performance.

The
ability of an open economy to effectively adopt and develop new ideas, in turn,
is likely to depend on the environment created by the level of education, the
legal system, the quality of administration and so on. This environment is what
the Doing Business rank is trying to capture.

Box 1.3. Chapters of the Ease of Doing Business index The World Bank's Ease of Doing Business attempts to measure some key elements of doing business, from the number of days required to start a business to the number of documents needed to export. This is a brief description of the contents of each section:

Figure 1.22 shows how
spread EU countries are in the Ease of Doing Business world rank. Greece ranked 109 out of 180 ranked countries, meaning that EU Member States are ranked
over the first two thirds of the support of the distribution. Below it is
discussed that this can be seen as room for easy improvements.

Figure 1.22. Ease of
Doing Business world rank, EU Member States

Source: World Bank,
Ease of Doing Business database.

In
Figure
1.23
a clear relation arises between the Doing Business rank and the level of income
per head. This scatter plot is most likely capturing something very relevant.[31]
The position in the rank entails large differences in the level of income per
head. It should be noted that the relation with growth is less obvious.
Correcting growth by the initial level of income (catching-up countries are
expected to grow faster), the relation with the Doing Business rank is quite
weak: at most slightly negative and with a large dispersion around the mean
relation.

Figure 1.23. Ease of Doing Business
and GDP per head

Note: The
change in GDP per head is adjusted by initial level of income to compensate the
fact that countries with a lower initial level of income tend to grow faster.

Source: World Bank,
Ease of Doing Business database and AMECO database, Commission services.

              1.9     Conclusions

Europe is the largest trading block in the
world. EU economies are characterized by a notable degree of openness: both
within the EU and by a strong integration in world markets. This chapter
suggests that a good export performance is mostly reflecting something that is
going well domestically: a buoyant economy able to produce commodities that
meet the test international markets. For instance, a good record of exports of
manufactures cannot be possible without a solid manufacturing base. Another way
to see it is to consider the connection between trade and overall economic
performance as conditional on many factors, most notably internal factors such
as the Ease of Doing Business. For foreign new ideas, techniques and machines
to impact the productivity, an economy must provide with the right incentives
to adopt these technologies, a sound financial system to fund new investments,
or the legal framework that eases the creation of new businesses.

This is not only a long-term issue. The elusive
recovery of income in many EU Member States despite the swift recovery of
exports during this recession points as well in the direction of the weight of
internal factors. To see this, note that
countries without internal imbalances, whose income is recovering from the
initial contraction, are also those countries in which imports are recovering
as fast as exports. Countries stagnating show a recovery of exports – external
demand is independent of internal developments – but not of imports or other
components of internal demand. It may be worth noting that an immediate
corollary to this observation is that devaluations are only one of the
instruments in the policy toolbox to fight the consequences of a recession.
Both euro and non-euro Member States are witnessing strong increases in
exports, but some countries see their income stagnate while others are
recovering fast, and this in both groups. Factors other than
price-competitiveness seem to be playing a determinant role.[32]

The importance of domestic conditions relative
and in combination to external performance has a different meaning depending
whether we focus in the short or in the long term. In the short-term, the
denouement of the recession requires internal imbalances to be corrected, in
particular leverage by private agents in countries with severe imbalances
accumulated. The role of policy there is to strike a delicate balance between
government finances equilibrium and stimulus measures to soften the impact of
the adjustment as much as possible. And of course, even if exports alone cannot
pull EU economies out of the recession, they constitute a precious positive stimulus.

In the long-run growth will be enhanced and
sustained by a combination of many factors, with openness and a
business-friendly environment being two key ingredients. In a time when
government finances are under stress, revising the regulatory environment or
increasing the efficiency of the administration alongside an ambitious external
trade agenda may be seen as cost-effective measures. The large impact of the
Doing Business rank in the level of income and the considerable heterogeneity
within the EU suggests that there being room for easy improvements, easy in the
sense that most chapters of the index concern regulation rather than
expenditures. Of course, it may not be "easy" in the sense that
vested interests may resist changes, but together with other far-reaching
reforms, like labour market of tax reforms, they may put the basis for strong
growth in the forthcoming years.

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on the Impact of MERCOSUR on Argentinian Firms´, American Economic Review,
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Costantini,
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Financial Affairs, European Commission.

European
Commission (2010a), Employment in Europe 2010, Directorate-General
Employment and Social Affairs, European Commission.

European
Commission (2010b), ´Surveillance of Intra-Euro-Area Competitiveness and
Imbalances´, Directorate-General Economic and Financial Affairs, European
Commission.

European
Commission (2012), Quarterly Report of the Euro Area, 11(1).

Hall,
R.E. (2005), ´Job Loss, Job Finding, and Unemployment in the U.S. Economy over
the Past Fifty Years´, NBER Macroeconomics Annual, 101-137.

Henning,
C.R. and Kessler, M. (2012), Fiscal federalism: US history for architects of
Europe's fiscal union, Bruegel.

Krugman,
P. (1978), ´The Theory of Interstellar Trade´, mimeo, Princeton University.

Legrain,
P. (2008), Immigrants: Your Country Needs Them, Abacus.

Lileeva,
A. and Trefler, D. (2010), ´Improved Access to Foreign Markets Raises
Plant-level Productivity... For Some Plants´, Quarterly Journal of Economics,
125(3), 1051-1099.

Melitz,
M.J. (2003), ´The impact of trade on intra-industry reallocations and aggregate
industry productivity´, Econometrica, 71(6), 1695-1725.

Reinhart,
C.M. and Rogoff, K.S. (2011), This Time Is Different: Eight Centuries of
Financial Folly, Princeton University Press.

UN
(2010), Manual on Statistics of International Trade in Services 2010.
United Nations.

Wolf,
M. (2012), ´What was Spain supposed to have done?´, Financial Times, 25
June 2012.

World
Bank (2012), Doing Business 2012. Doing Business in a More Transparent World.
World Bank.

Appendix.
Statistics

Table 1.7. Changes in GDP components during the
recession

Source: AMECO
database, Commission services, and own calculations

Table 1.8. The average weight of services in
total exports

Source: AMECO
database, Commission services.

2. The EU Industry in the Global Value
Chain

On-going globalisation
has changed the economic landscape. Many products used to be produced locally
from mainly domestic resources. This meant that most of the value chains or
production processes were located in the country where firms had their
headquarters. Technological development has facilitated the geographical
fragmentation of production processes, resulting in the emergence of global
value chains. Different parts of firms’ production processes are now located in
different parts of the world, according to the comparative advantages of the
locations. This ‘slicing up of the value chains’ has given rise to increased
trade flows of goods and services in the world economy. A large share of this
trade is intra-firm trade in intermediate goods, conducted by multinational companies.
The use of imported intermediate goods in manufacturing industries has
increased globally, thereby involving more industries and countries in the
value chains.

The
increasingly important role of global value chains for the EU industry is
emphasised in the EU flagship initiative ‘An integrated industrial policy for
the globalisation era’ which states: ‘The EU needs to pay greater attention to
the manufacturing value-chain … Industry is increasingly dependent on inputs of
raw material and intermediate goods, and is also crucially dependent on the
business services industries that add value and help to design and market new
goods and services. This new perspective requires a different approach to industrial
policy that takes increased account of the interlinkages’ (European Commission,
2010). This initiative identifies a number of policy areas that would help EU
firms to reap the benefits of globalisation and to compete on global markets.
The design of appropriate policies requires better understanding of the
development and prospects of global industrial value chains. This chapter tries
to respond to this need by looking for empirical answers to the following
questions:

·
What
have been the main changes in industries’ value chains since 1995?

·
How
have the inter-industry and inter-regional linkages within the EU and in
extra-EU relations developed?

·
How
do these compare with inter-industry and inter-regional linkages in the US, Japan and other countries?

·
What
was the impact of the 2008/09 economic recession on the offshoring decisions of
EU firms?

·
What
are the effects of the crisis on vertical specialisation and value chains in
industries producing chemicals, machinery and equipment, electrical and optical
equipment and transport equipment?

·
What
types of firms are more likely to offshore parts of their supply chain?

·
What
leads firms to offshore and what drives the decisions with respect to the
characteristics of the host and destination country and those of the offshoring
firms?

·
What
are the preferred target countries for relocating production for European
manufacturing companies?

·
Is
offshoring related to framework conditions in the different locations?

These questions are addressed by focusing largely on four
important manufacturing industries, classified according to NACE Rev. 1.1: chemicals,
chemical products and man-made fibres (DG); machinery and
equipment (DK); electrical and optical equipment (DL); and transport equipment
(DM). The first questions are addressed in Section 2.2, which analyses patterns
and trends in vertical specialisation across countries. The analyses for the
four selected industries are preceded by overviews of the patterns for total
exports, manufacturing exports and services exports. Section 2.3 focuses on the
changes in trade patterns of the four individual manufacturing industries by
geography. The analyses differentiate between the use categories of products:
trade in parts and components is important for industries producing machinery
and equipment, electrical and optical equipment and transport equipment, while
trade in semi-finished products is important for the chemicals industry.
Section 2.4 focuses on offshoring decisions at company level; it contains
analyses of the motives and determinants of company strategies with respect to
the relocation of production. A summary and conclusions are provided in Section
2.5.

              2.1 The many facets of international production integration

Many different concepts are used in analysing the internationalisation
of production. Examples include ‘global production sharing’, ‘(international)
fragmentation’, ‘slicing up the value chain’, ‘vertical specialisation’,
‘international (out)sourcing’, ‘offshoring’, ‘global supply chains’, ‘global
value chains’, etc. Here, an account of the most widely used categories is given.
A rigorous, precise and accurate definition is used as a starting point, and
other categories are related to that. ‘Offshoring’ and ‘offshore outsourcing’
refer to a company’s decision to transfer certain activities that have so far
been carried out inside the company to either another unit of the firm in a
foreign location (intra-firm or captive offshoring) or to an independent firm
(offshore outsourcing). Offshoring and offshore outsourcing are sometimes
referred to as (international) relocation (OECD, 2004; UNCTAD, 2004;
Kirkegaard, 2005). These and related terms are used in rather an unsystematic
way in the literature — something that needs to be considered in any
discussion.[33]

Table
2.1 – Understanding intra-firm or captive offshoring, outsourcing and offshore
outsourcing

Location of production || Internalised (inside the company) || Externalised (outside the company, outsourcing to an independent firm)

Home country || Production kept in-house at home || Outsourcing (at home)

Foreign country (offshoring) || Intra-firm (captive) offshoring || Offshore outsourcing

Source: UNCTAD (2004).

‘Offshoring’
is also widely used to denote the relocation of processes to foreign countries,
regardless of their links to the relocating company (see, for example, Olsen,
2006; Bertoli, 2008; Jabbour, 2010). In this case, attention is focused only on
the movement of production and related jobs between countries. Similarly, some
papers make no distinction between offshoring and offshore outsourcing: they
are usually both referred to as offshoring (see, for example, Görg et al.,
2008; Wagner, 2011). Here again the emphasis is on the moving of the activities
abroad from the home country.[34]

Other
approaches rely on various trade data to analyse changes in the structure of
global production and the increase in trading links across countries. One such
approach concerns the trade in parts and components. Yeats (1997) was the first
to use these data to try and measure the phenomenon; he called it ‘production
sharing’. Other studies with the same approach include Ng and Yeats (1999) and
Kaminski and Ng (2001). Trade in intermediates is a similar concept often used
in empirical analyses on which other approaches are based on. International
fragmentation (e.g. Jones and Kierzkowski, 1990) places more emphasis on
production activities, with fragmentation being defined as the splitting of
production processes into parts that can be done in different countries (see,
for example, Baldone et al., 2001, in the European context).[35] Vertical
specialisation (Hummels et al., 2001) is based on trade between different
countries, each specialising in a particular production stage. The authors make
the connection between the fragmentation of production and exports by sector by
calculating direct and indirect (through suppliers) imports that are then
incorporated into the exports of a given country, in order to determine that
country’s specialisation.

International
‘trade in tasks’ (reflecting a finer division of labour across countries) — as
opposed to trade in finished goods (e.g. Grossman and Rossi-Hansberg, 2008) —
refers to captive offshoring and offshore outsourcing. This approach is used in
many theoretical models.

Furthermore,
two further concepts describe the phenomenon of Western European firms
concentrating their offshoring and offshore outsourcing activities in Central
and Eastern Europe (Jacoby, 2010). ‘Nearshoring’ — as opposed to ‘farshoring’ —
emphasises the geographical proximity between the offshoring and outsourcing
company and its affiliate/partner. ‘Nearsourcing’ is used as an equivalent to
‘nearshoring’ (ACM, 2006). For example, in the US, ‘nearshoring’ is referred to
in the context of relocations to Canada or Mexico (Olsen, 2006). Similarly, in
Europe, ‘nearshoring’ is usually used in the context of offshoring and offshore
outsourcing to Central and Eastern Europe. A key aspect of nearshoring is the
fact that global value chains are more regional than global (De Backer and
Yamano, 2011). The term ‘backshoring’ or ‘reshoring’ is used when previously
captive offshored or offshore outsourced activities are brought back to the
original location.

As is obvious
from the existing diversity of definitions, the old approaches and the
widely-used existing data are not considered adequate or appropriate to grasp
all the aspects of this phenomenon. For example, at the macro-level, the
concepts ‘offshore outsourcing’ and ‘offshoring’ are differently connected to
foreign direct investment (FDI) and foreign trade. Offshore outsourcing is
usually not connected to FDI, but is usually connected to international trade.
In the case of captive offshoring, an initial FDI project of the vertical type
is always involved, and later the output is exported to other affiliates and
sold to the local affiliate of the same company. In captive offshoring all
these transactions remain within the boundaries of the company, in contrast to
offshore outsourcing. So both flows of FDI and foreign trade are involved.

Thus neither
the available FDI data nor the foreign trade data are able to fully cover
developments connected to offshoring and offshore outsourcing. It must also be
emphasised that widely-used measurements based on trade statistics should be
used with caution. It could be misleading to use trade statistics designed to
collect trade flows in final products, because of the increase of trade in
parts and components or intermediaries. For example, revealed comparative
advantage indicators, specialisation indices or classification according to the
technology content of products may give an erroneous result concerning the
specialisation and role of a given country in the international distribution of
labour.

Different methods are
applied in this chapter to take account of the many aspects of the
internationalisation of production. Section 2.3 builds on the measurement of
vertical specialisation, which is derived from a global input-output matrix
combining industry-level information on sourcing structures with detailed trade
data. Section 2.4 is based on trade data that differentiate between the various
end-use categories of traded products, which allows the effects of the crisis
to be captured. Finally, Section 2.5 builds on firm-level data to shed light on
micro-economic aspects of the internationalisation process.

              2.2. Changes in industries' value chains since 1995

International
linkages vary across industries, and change over time. Not only do countries
have to rely on imports of products not produced domestically, e.g. raw materials,
but industries are likely to participate in the international division of
labour, by offshoring the production of semi-finished products or via inputs of
parts and components or assembly activities. This section analyses vertical
specialisation patterns and the respective changes over time for EU-27
industries, drawing comparisons with the US and Japan in the period from 1995
until recent years. Particular questions to be addressed are whether and to
what extent the import content of exports has changed over the longer term and
in more recent years? Have there been any major shifts with respect to source
patterns by geographical regions, and are there significant differences across
countries? Have the industries examined in more detail here faced significant
changes in vertical specialisation patterns compared to overall patterns?

Methodologically,
the chapter builds on the measurement of vertical specialisation developed by
Hummels et al. (2001). It uses a global input-output table, which provides a
more precise metric of vertical specialisation. The use of a global
input-output table allows for not only differentiating direct imports from
different countries but also indirect imports from different countries arising
from the flows of intermediate goods in different parts of the value chains.  The
data used for this section are the world input-output tables from the World
Input-Output Database (WIOD) project, which have recently become available.[36]

This approach
facilitates more detailed analyses of changes in the international sourcing
structures. By using information from the WIOD it is possible to analyse the
structures of sourcing and vertical specialisation. Hummels et al. (2001)
recommended a widely used measure of vertical integration, which has subsequently
been extended and made more sophisticated. In this study, a slightly more
generalised measure of vertical integration is used, which takes full advantage
of a global input-output table. A global matrix such as this allows the
calculation of the global Leontief inverse matrix, from which a vertical
specialisation indicator can be calculated. Such a measure of vertical
specialisation is closely related to the concept of output multipliers, and
therefore also to backward (and forward) linkage indicators, cf. Box 2.1.[37]

Box 2.1 – A generalised
measure of vertical specialisation

The
most widely used measure of vertical specialisation is the VS measure proposed
in Hummels et al. (2001) which pre-multiplies the domestic Leontief inverse by
the import coefficients matrix and expresses the resulting matrix sum as a
ratio to total gross exports.[38]
A more sophisticated measure, VS1, pre-multiplies the domestic Leontief inverse
by the import matrices for each individual partner country; the results are
then summed together and expressed as a ratio to total gross exports.

These
measures, however, do not take account of all inter-country linkages, i.e.
imports from a country might (directly and indirectly) include imports from
other countries, or even the country under consideration. The availability of a
world input-output table therefore allows these inter-regional linkage effects
to be taken into account. This would suggest an appropriate indicator – VS2 –
using the Leontief inverse of the global input-output table times the vector of
exports of the reporter country under consideration and summed over all partner
countries. This can be expressed as a share of total gross output produced for
production of this export vector. Formally, this can be expressed as

Let
C denote the number of countries and N the number of industries. The vector  denotes an
NCx1 vector with country r’s exports included in the appropriate
elements of the vector and zeros otherwise. The vector  denotes a summation
vector (of dimension NCx1) with 0 in country r’s appropriate elements of
the vector and 1 otherwise, i.e. summing over all partner countries. Similarly,
 denotes a
summation vector of ones of dimension NCx1, summing over all countries. Matrix A
denotes the coefficient matrix, i.e. inputs per unit of gross output, and I
is the identity matrix, both are of dimension NCxNC. The prime indicates the
transpose of the respective vectors.

When
examining particular regions or sectors, the summation and export vectors  and   have to be
adjusted accordingly (i.e. summing up over only those partner countries that
are of interest). In case that one is interested in only one particular
industry the export vector contains exports of this industry only and 0’s otherwise
and the summation vector  contains a
one for that industry and 0’s otherwise. Using gross output associated
with the production of the particular exports, i.e.  the sourcing
structure to produce a particular vector of exports is expressed as a
percentage of total production needed for these exports. This can further be
broken down by individual partner countries or groups of partner countries.

Multiplying
the Leontief inverse by the total export vector, including the intermediates,
involves a certain degree of ‘double-counting’. One possibility to remedy this
would be to use exports of final demand goods only. Empirically, it does not
make a big difference when expressed as a share of gross output to be produced,
however, and is more akin to the original measure proposed in Hummels et al.
(2001). It should be noted that this measure is closely linked to the linkage
indicators – or, more specifically, to the backward linkage measure – and the
concept of (simple output) multipliers, which are also based on the Leontief
inverse. Therefore, one would expect, first, a country to be more vertically
integrated the higher its (backward) linkages. If this country’s output should
increase (e.g. by assembly of final products), it needs more inputs from other
countries, and thus its backward linkages are higher and it is more vertically
integrated.

Secondly,
this also explains why larger countries tend to be less vertically integrated
in the global economy, since large countries source relatively more from their
domestic economy. Conversely, smaller countries are not able to produce all the
inputs themselves and thus tend to be more vertically integrated. For a more
detailed discussion, see Stehrer et al. (2012a) and the literature cited
therein.

              2.2.1  International linkages and the foreign content of exports

The aggregate
results for EU, US and Japan  are presented before the four selected industries
are analysed. For the economy-wide analyses, the EU-27 is split into the EU-15
and the EU-12, as the latter group shows a particular pattern in the European
division of labour. The EU-15, Japan and the US show initial low levels for the
foreign content of exports of between 5 % and 10 %. In 1995 the figure
for the US was comparable to that for the EU-15 in 2000. The vertical
specialisation is higher in the EU-12 countries and, even in 1995, the EU-12
countries had a much higher vertical specialisation than the other countries.
This was partly due to the strong backward linkages these countries already had
as providers of intermediate inputs for (mainly) the EU-15, but was also due to
the fact that the country group consists of relatively small countries. Their
integration intensified even further over time, peaking in 2007 at about 34 %.

In the three
other countries and regions, the foreign content of exports increased to levels
of about 14–16 %. The particularly strong increase experienced in the
EU-12 countries points to the strong integration process with the EU since
1995, generated especially by production networks.

During
the recent economic crisis, however, the foreign content dropped slightly, by
1–2 percentage points, in three of the regions. As the data end in 2009, this
drop might also have been driven by an industry composition effect, since it
was particularly sectors with stronger production linkages that were affected
more severely by the crisis. The decrease was even stronger for the EU-12
countries, with a drop of about 4 percentage points.

Figure
2.1 Foreign content of total exports (%)

Source:
WIOD.

Breaking down
Figure 2.1 by source region shows how the sourcing structure at economy-wide
levels has changed over time. Table 2.2 provides information on the
geographical structure of the foreign content of exports across source regions
over time for the EU, Japan and the US.

The
table shows the foreign content of exports and the domestic content highlighted
in grey. As shown, the domestic content is relatively high in all countries: it
is lowest in the EU-12, standing at 66.4 % in 2007, and higher for the
other economies: around 85 %. In all cases, the domestic share has
decreased.

Table 2.2
– Content of total exports, by partner

|| EU-12 || EU-15

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 3.1 || 2.8 || 2.6 || 2.6 || 2.1 || 0.8 || 0.9 || 1.3 || 1.5 || 1.3

Canada || 0.2 || 0.2 || 0.2 || 0.3 || 0.2 || 0.3 || 0.3 || 0.3 || 0.3 || 0.3

China || 0.2 || 0.8 || 2.1 || 3.4 || 4.8 || 0.4 || 0.8 || 1.3 || 2.0 || 2.8

EU-12 || 79.0 || 70.2 || 68.4 || 66.4 || 70.1 || 0.6 || 0.9 || 1.3 || 1.6 || 1.6

EU-15 || 13.1 || 18.4 || 18.6 || 18.6 || 15.7 || 92.0 || 88.8 || 87.8 || 86.0 || 86.8

Japan || 0.5 || 1.1 || 1.1 || 1.2 || 0.9 || 1.0 || 1.1 || 0.8 || 0.8 || 0.7

Korea || 0.3 || 0.5 || 0.7 || 0.9 || 0.8 || 0.3 || 0.4 || 0.5 || 0.4 || 0.4

Mexico || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.1 || 0.2 || 0.2 || 0.2 || 0.1

USA || 1.1 || 1.9 || 1.4 || 1.4 || 1.3 || 1.8 || 2.5 || 1.8 || 1.9 || 1.8

Rest of world || 2.4 || 4.0 || 4.7 || 5.1 || 4.0 || 2.8 || 4.1 || 4.6 || 5.2 || 4.3

|| || || || || || || || || ||

|| Japan || USA

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 0.5 || 0.5 || 0.8 || 1.1 || 0.9 || 0.4 || 0.5 || 0.7 || 0.8 || 0.7

Canada || 0.2 || 0.2 || 0.2 || 0.2 || 0.2 || 1.4 || 1.6 || 1.7 || 1.7 || 1.4

China || 0.5 || 0.9 || 2.2 || 3.1 || 3.8 || 0.6 || 0.9 || 2.0 || 2.7 || 3.3

EU-12 || 0.0 || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.1 || 0.2 || 0.2 || 0.1

EU-15 || 1.4 || 1.7 || 2.1 || 2.4 || 1.9 || 2.8 || 3.1 || 3.4 || 3.3 || 2.7

Japan || 93.3 || 91.3 || 87.8 || 84.7 || 86.2 || 1.9 || 1.6 || 1.3 || 1.2 || 0.9

Korea || 0.6 || 0.7 || 0.9 || 1.1 || 0.7 || 0.5 || 0.6 || 0.6 || 0.5 || 0.4

Mexico || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.6 || 0.9 || 0.9 || 1.0 || 0.9

USA || 1.3 || 1.5 || 1.3 || 1.5 || 1.2 || 89.0 || 87.5 || 85.7 || 84.8 || 86.3

Rest of world || 2.1 || 3.0 || 4.5 || 5.6 || 4.9 || 2.6 || 3.1 || 3.6 || 3.9 || 3.1

Note: BRII comprises
Brazil, Russia, India and Indonesia.

Source:
WIOD..

The financial
crises had a severe impact on global trade and thus also on the trend of
increased vertical specialisation. In order to analyse the long-term trends,
the year 2009 has therefore been omitted from the following analysis. In 2007,
the BRII group accounted for about 10 % or less of the import content of
most countries, with a larger share for the EU-15. It is interesting to note
that this group — although it includes India, which is comparable in size to
China — does not account for higher shares of vertical integration,
particularly not where the US is concerned. Canada is important for the US, even more so than Mexico. China accounts for about 10 % of the foreign content of exports
in the EU-12, 15 % in the EU-15, 20 % in Japan and about 18 % in
the US. China has surpassed the EU-12 as a source for the EU-15 in recent
years. The EU-12 countries are only important as a source for the EU-15, where
it accounts for about 12 %. On the other hand, the EU-15 countries are
very important for the EU-12, which use a lot of EU-15 outputs to produce their
own exports.

The EU-15
accounts for about 16 % and 20 % of the foreign content of Japanese
and US exports. The EU-15 share of Japanese exports decreased from 1995 to
2007. The Japanese share of EU-15 and US exports decreased from 1995 to 2007,
the largest declines being recorded for exports to the US. As can be expected, the US is the main market for Mexico, making up about 5 % of its export
content, but the figure is considerably smaller for the other countries under
consideration. Finally, US output accounts for about 13 % of the foreign
content of EU-15 exports and 10 % of Japan’s. The content of exports from
the rest of the world (ROW) is particularly high in the EU-15 and Japan. It should be noted that the ROW includes countries like Switzerland and Norway and Turkey, which have strong trade relations with the EU countries. On the other hand, the
ROW group includes a number of Latin and South American countries, important
for the US, and a host of Asian countries with strong production networks,
important for Japan.

The most
impressive development has been the rise in the importance of China. The Chinese share of the foreign content of EU-12 exports increased from a negligible figure
in 1995 to 10 % in 2007. Its share of EU-15 exports increased from
slightly above 5 % to about 15 %. The increase was even more marked
in Japan, where China’s share rose from about 7 % to 20 %, cf. Table
2.3.

Table
2.3. Geographical structure of the foreign content of exports, 1995 and 2007

|| 1995 || 2007

|| EU-12 || EU-15 || Japan || USA || EU-12 || EU-15 || Japan || USA

BRII || 15.0 || 10.4 || 7.3 || 3.9 || 7.7 || 11.0 || 7.1 || 5.2

Canada || 0.7 || 3.3 || 3.2 || 13.0 || 0.8 || 2.2 || 1.6 || 11.4

China || 1.2 || 5.4 || 7.4 || 5.5 || 10.2 || 14.5 || 20.0 || 17.5

EU-12 || - || 7.8 || 0.5 || 0.8 || - || 11.5 || 1.0 || 1.2

EU-15 || 62.4 || - || 21.5 || 25.7 || 55.3 || - || 15.9 || 21.6

Japan || 2.4 || 11.9 || - || 17.4 || 3.5 || 5.9 || - || 7.7

Korea || 1.4 || 3.2 || 8.4 || 5.0 || 2.8 || 3.1 || 7.1 || 3.5

Mexico || 0.2 || 1.1 || 0.6 || 5.3 || 0.4 || 1.2 || 0.7 || 6.4

USA || 5.3 || 22.0 || 19.1 || - || 4.1 || 13.4 || 9.9 || -

ROW || 11.4 || 34.8 || 32.0 || 23.4 || 15.3 || 37.3 || 36.8 || 25.5

Note:
BRII comprises Brazil, Russia, India and Indonesia. The
columns sum to 100.

Source: WIOD.

The increase
in the Chinese share from 1995 to 2007 may have taken place at the expense of
other foreign sources or domestic sourcing. Table 2.4 below, which presents the
changing share pattern in percentage points, can be used to analyse whether the
rise of China in world trade and vertical specialisation has been at the
expense of other countries.

With a few
exceptions, the changes are positive, implying that, in terms of vertical
specialisation, partner countries did not crowd each other out; instead China’s share grew mainly at the expense of domestic sourcing in the period 1995–2007.

The Chinese
share of other countries exports increased until 2007 and continued to grow
during the crisis (up to 2009, the last year for which data are available).
However, the overall share of the foreign content of exports decreased between
2007 and 2009. For example, in the EU-12, domestic sourcing increased by about
4 percentage points; in the EU-15 it increased by less than 1 percentage point
and in the US and Japan domestic sourcing increased by about 1.5 percentage
points, c.f. Table 2.4.

Table 2.4 – Changes in
the geographical structure of production integration (percentage points).

|| 1995–2007 || 2007–09

|| EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA

BRII || -0.6 || 0.7 || 0.6 || 0.4 || -0.5 || -0.2 || -0.2 || -0.1

Canada || 0.1 || 0.0 || 0.0 || 0.3 || -0.1 || -0.1 || 0.0 || -0.3

China || 3.2 || 1.6 || 2.6 || 2.0 || 1.3 || 0.7 || 0.8 || 0.7

EU-12 || -12.7 || 1.0 || 0.1 || 0.1 || 3.7 || 0.0 || 0.0 || 0.0

EU-15 || 5.5 || -5.9 || 1.0 || 0.5 || -2.9 || 0.8 || -0.6 || -0.5

Japan || 0.7 || -0.1 || -8.6 || -0.7 || -0.2 || -0.2 || 1.5 || -0.3

Korea || 0.6 || 0.2 || 0.5 || 0.0 || -0.1 || 0.0 || -0.4 || -0.1

Mexico || 0.1 || 0.1 || 0.1 || 0.4 || 0.0 || 0.0 || 0.0 || -0.1

USA || 0.3 || 0.1 || 0.2 || -4.2 || -0.1 || 0.0 || -0.3 || 1.6

Rest of world || 2.7 || 2.4 || 3.5 || 1.3 || -1.2 || -0.9 || -0.7 || -0.7

Note: BRII comprises
Brazil, Russia, India and Indonesia. The columns sum to 0.0.

Source: WIOD.

Before
analysing the four selected industries, an overview is provided of changes in
the vertical specialisation in manufacturing and services. As in the case of
total exports, the degree of vertical specialisation in the EU-12 is relatively
high. This is mostly due to the strong backward linkages with industries in the
EU-15. Starting at lower levels, the foreign content of exports in EU-15 and
Japanese industries increased to around 8 % in 2009. The crisis seems not
to have had as big an impact on the global value chains of EU-15 services as it
has in the other regions. A small increase was recorded for the EU-15 between
2007 and 2009, due to the increased share of Chinese production in EU-15
services exports. The foreign content of Japanese exports, which increased
rapidly up to 2007, was severely hit by the crisis and decreased by some 3
percentage points between 2007 and 2009. The decrease can largely be explained
by the large fall in Japanese services exports. Consequently, the share of
services of total exports also decreased. The largest decreases were recorded
in the sectors Water transport and Wholesale trade and commission trade,
NACE codes 61 and 51 respectively, which account for a relatively large
proportion of Japanese services. The decrease in the foreign content of
Japanese exports mostly affected EU-15 and Korean producers, c.f. Figure 2.2.[39]

Figure
2.2 Foreign content of services exports (%)

Source:
WIOD.

The foreign
content of manufacturing exports is higher than for total exports and
services exports in all countries and regions. The largest differences in the
degree of foreign content of exports between the total economies and the
manufacturing industries are seen in the EU-12 and the US. The strong backward linkages between the EU-12 and EU-15 are mainly due to EU-12 manufacturing
industries providing intermediate inputs for manufacturing to the EU-15. Large
multinational enterprises in the US manufacturing sector account for much of
the foreign content of total US exports. Domestic sourcing in Japanese
manufacturing industries did not increase as much as in the services
industries. The increase was more in line with the other regions.

Since most of
the vertical specialisation process takes place within manufacturing
industries, developments over time for manufacturing exports reflect the
development over time for total exports. Domestic sourcing decreased from 1995
to 2007 but increased from 2007 to 2009, with the exception of Chinese
sourcing, c.f. Table 2.5.

Table
2.5 – Content of manufacturing exports, by partner

|| EU-12 || EU-15

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 3.5 || 2.8 || 2.7 || 2.7 || 2.3 || 0.9 || 1.0 || 1.5 || 1.8 || 1.5

Canada || 0.2 || 0.2 || 0.2 || 0.3 || 0.2 || 0.3 || 0.3 || 0.3 || 0.3 || 0.3

China || 0.3 || 0.3 || 2.5 || 4.0 || 5.7 || 0.5 || 0.9 || 1.6 || 2.4 || 3.3

EU-12 || 76.7 || 66.6 || 65.0 || 62.6 || 66.2 || 0.7 || 1.0 || 1.5 || 1.9 || 1.9

EU-15 || 14.7 || 20.9 || 20.8 || 20.8 || 17.7 || 91.2 || 87.7 || 86.4 || 84.1 || 85.0

Japan || 0.6 || 1.3 || 1.3 || 1.3 || 1.1 || 1.1 || 1.3 || 0.9 || 0.9 || 0.8

Korea || 0.3 || 0.5 || 0.8 || 1.1 || 1.0 || 0.3 || 0.4 || 0.5 || 0.5 || 0.5

Mexico || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.1 || 0.2 || 0.2 || 0.2 || 0.1

USA || 1.1 || 2.0 || 1.4 || 1.5 || 1.4 || 1.9 || 2.6 || 1.9 || 1.9 || 1.9

Rest of world || 2.6 || 4.4 || 5.1 || 5.6 || 4.4 || 3.1 || 4.6 || 5.2 || 5.9 || 4.9

|| || || || || || || || || ||

|| Japan || USA

|| 1995 || 2000 || 2005 || 2007 || 2009 || 1995 || 2000 || 2005 || 2007 || 2009

BRII || 0.5 || 0.5 || 0.8 || 1.2 || 1.0 || 0.5 || 0.7 || 0.9 || 1.0 || 0.8

Canada || 0.2 || 0.2 || 0.2 || 0.2 || 0.2 || 1.8 || 2.0 || 2.2 || 2.2 || 1.9

China || 0.5 || 1.0 || 2.3 || 3.3 || 4.0 || 0.8 || 1.2 || 2.7 || 3.5 || 4.5

EU-12 || 0.0 || 0.1 || 0.1 || 0.2 || 0.1 || 0.1 || 0.1 || 0.2 || 0.2 || 0.2

EU-15 || 1.5 || 1.8 || 2.2 || 2.5 || 2.0 || 3.4 || 3.8 || 4.3 || 4.2 || 3.5

Japan || 93.1 || 91.1 || 87.3 || 84.0 || 85.6 || 2.5 || 2.1 || 1.7 || 1.6 || 1.2

Korea || 0.5 || 0.6 || 0.9 || 1.0 || 0.7 || 0.7 || 1.1 || 0.8 || 0.7 || 0.6

Mexico || 0.0 || 0.1 || 0.1 || 0.1 || 0.1 || 0.8 || 1.1 || 1.2 || 1.3 || 1.2

USA || 1.4 || 1.6 || 1.4 || 1.6 || 1.2 || 86.2 || 84.4 || 81.8 || 80.7 || 82.3

Rest of world || 2.2 || 3.1 || 4.7 || 5.9 || 5.1 || 3.1 || 3.7 || 4.3 || 4.5 || 3.8

Note:
BRII comprises Brazil, Russia, India and Indonesia. The table shows the foreign content of
exports and the domestic content highlighted in grey.

Source: WIOD.

When looking
at the four selected industries, it is evident that vertical integration of the
EU-12 industries is higher than that of other countries. This is to be expected
due to strong production and backward linkages in the EU: an increase in the
output of a final product in an EU-12 country triggers significant demand in
other sectors and in EU-15 countries, implying strong backward linkages. The
integration of production in the EU-12 industries — indicated by a low domestic
share in Table 2.6 — is particularly strong in electrical products and
transport equipment, and only slightly weaker in machinery. It is far lower in
chemicals, whose production relies less on intermediates sourced from other
countries. The EU-15, Japanese and US industries show fairly similar vertical
integration patterns, though these tend to be somewhat lower for Japan in most industries. Generally, vertical integration is relatively higher in machinery
and transport equipment, i.e. industries characterised by larger international
production networks.

Table 2.6 – Vertical integration, 2007, in %

|| Chemical, chemical products and man-made fibres || Machinery and equipment || Electrical and optical equipment || Transport equipment

|| EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA

BRII || 5.0 || 1.7 || 1.3 || 0.9 || 2.1 || 1.4 || 1.1 || 1.2 || 1.6 || 1.3 || 1.0 || 0.9 || 1.7 || 1.4 || 0.9 || 1.0

Canada || 0.2 || 0.3 || 0.3 || 1.8 || 0.3 || 0.3 || 0.2 || 2.1 || 0.3 || 0.3 || 0.2 || 1.4 || 0.3 || 0.4 || 0.2 || 3.3

China || 1.5 || 1.3 || 2.1 || 1.7 || 2.7 || 2.5 || 3.7 || 3.8 || 9.6 || 4.9 || 4.8 || 6.3 || 2.8 || 2.3 || 2.8 || 3.7

EU-12 || 67.6 || 1.1 || 0.1 || 0.2 || 63.7 || 2.2 || 0.1 || 0.3 || 52.6 || 2.3 || 0.1 || 0.3 || 59.1 || 2.8 || 0.2 || 0.3

EU-15 || 17.4 || 86.0 || 3.0 || 5.4 || 22.4 || 85.5 || 2.4 || 4.4 || 21.7 || 81.3 || 2.2 || 3.5 || 26.8 || 83.8 || 3.0 || 5.3

Japan || 0.6 || 0.7 || 82.6 || 0.9 || 1.2 || 1.0 || 84.8 || 1.6 || 2.7 || 1.4 || 83.1 || 1.6 || 1.7 || 1.5 || 86.7 || 3.1

Korea || 0.4 || 0.3 || 0.7 || 0.4 || 0.8 || 0.5 || 1.2 || 0.8 || 2.4 || 0.9 || 1.4 || 1.1 || 1.3 || 0.7 || 0.8 || 1.1

Mexico || 0.1 || 0.1 || 0.1 || 0.6 || 0.1 || 0.2 || 0.1 || 1.2 || 0.2 || 0.2 || 0.1 || 1.7 || 0.2 || 0.3 || 0.1 || 1.8

USA || 1.3 || 2.4 || 1.7 || 83.6 || 1.4 || 1.7 || 1.7 || 80.8 || 2.2 || 2.4 || 1.9 || 78.8 || 1.6 || 2.4 || 1.6 || 76.9

Rest of world || 5.9 || 6.0 || 8.1 || 4.7 || 5.4 || 4.7 || 4.8 || 3.9 || 6.7 || 5.0 || 5.1 || 4.5 || 4.6 || 4.3 || 3.6 || 3.5

Note: BRII comprises
Brazil, Russia, India and Indonesia.

Source: WIOD.

With respect
to geographical structure, foreign partners’ shares of exports in the four
selected industries in 2007 are presented in Figure 2.3. The EU-12 sourced most
of their intermediates from the EU-15, with significant input also from China in electrical products and from BRII in chemicals. Japan also had a slightly larger
share than other industries. It is interesting to note that the EU-12 share is
no more than 20 % for these industries, which serves to illustrate the
EU-12’s strong backward linkages with respect to the EU-15, and the EU-15’s
weaker backward linkages with respect to the EU-12. The highest EU-12 share of
EU-15 exports is in transport equipment where there are strong international
networks in the motor vehicles industry. Intermediates from the US and China, especially in electrical products, account for large shares of EU-15 industrial
exports. Japanese intermediates account for a smaller share of EU-15 industrial
exports. China, the EU-15 and, to a lesser extent, the US are the main sources for Japanese industries. The large shares of intermediates sourced from the
ROW should be noted. These reflect the importance of South-East Asian production
networks for Japanese industries. The relatively high Korean share in Japanese
industries illustrates this phenomenon. Finally, important shares for the US industries can be seen for Canada and the EU-15. The EU-15 share of US exports is higher than the
corresponding US share of EU-15 exports. Mexican industries seem less
integrated in US industries’ value chains than their Canadian counterparts. An
exception is the relatively high share of Mexican sourced intermediates in US
electrical products exports. The rest of the world also provides inputs, with a
share of about 20 % on average.

Figure 2.3 – Geographical structure of the
foreign content, by industry, 2007

Note: BRII comprises Brazil, Russia, India and
Indonesia.

Source: WIOD.

The change in
sourcing patterns in 1995–2007 and 2007–2009, is similar to that for the total
economy discussed above. In particular, over the period 1995–2007, other
partners were not squeezed out. Instead sourcing from other countries increased
with foreign intermediates substituting for domestic intermediates. On the
other hand, domestic share increased at the expense of that of other countries over
the crisis period, with the exception of Chinese intermediates. Particularly
strong declines were observed in the EU-12. Due to the strong backward linkages
of these countries and low demand for products assembled in the EU-12, the
demand for EU-15 components fell, c.f. Table 2.7.

Table 2.7 – Changes in geographical sourcing
patterns (in percentage points)

|| Chemicals, chemical products and man-made fibres || Machinery and equipment || Electrical and optical equipment || Transport equipment

|| EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA || EU-12 || EU-15 || JPN || USA

|| 1995–2007

BRII || 1.1 || 0.8 || 0.8 || 0.5 || -0.1 || 0.7 || 0.6 || 0.6 || 0.1 || 0.7 || 0.6 || 0.4 || -0.3 || 0.6 || 0.5 || 0.5

CAN || 0.1 || 0.1 || 0.0 || 0.5 || 0.1 || 0.0 || 0.0 || 0.4 || 0.2 || 0.0 || 0.0 || 0.2 || 0.1 || 0.1 || 0.0 || 0.2

CHN || 1.3 || 1.1 || 1.9 || 1.3 || 2.5 || 2.1 || 3.2 || 3.0 || 9.2 || 4.2 || 4.2 || 5.2 || 2.5 || 1.9 || 2.4 || 2.9

EU-12 || -9.1 || 0.5 || 0.1 || 0.1 || -12.7 || 1.4 || 0.1 || 0.2 || -17.6 || 1.7 || 0.1 || 0.2 || -13.3 || 2.0 || 0.2 || 0.2

EU-15 || 3.5 || -6.0 || 1.2 || 1.8 || 5.9 || -6.3 || 1.1 || 0.6 || 1.3 || -7.3 || 1.0 || 0.0 || 6.9 || -7.2 || 1.0 || 1.0

JPN || 0.1 || -0.1 || -10.6 || -0.5 || 0.6 || -0.1 || -9.0 || -1.0 || 1.5 || -0.5 || -9.8 || -1.8 || 0.5 || -0.2 || -7.1 || -1.0

KOR || 0.1 || 0.1 || 0.4 || 0.0 || 0.5 || 0.2 || 0.6 || 0.2 || 1.8 || 0.4 || 0.7 || -0.3 || 0.7 || 0.4 || 0.5 || 0.3

MEX || 0.1 || 0.1 || 0.0 || 0.2 || 0.1 || 0.1 || 0.1 || 0.5 || 0.1 || 0.1 || 0.1 || 0.8 || 0.1 || 0.2 || 0.1 || 0.7

USA || 0.1 || 0.5 || 0.4 || -6.1 || 0.1 || -0.1 || 0.4 || -5.9 || 0.0 || -0.5 || 0.2 || -4.6 || 0.3 || 0.4 || 0.2 || -6.0

ROW || 2.7 || 2.8 || 5.8 || 2.2 || 3.0 || 2.0 || 2.8 || 1.4 || 3.4 || 1.4 || 3.0 || 0.0 || 2.5 || 1.9 || 2.2 || 1.1

|| 2007–09

BRII || -0.4 || -0.2 || -0.2 || 0.0 || -0.6 || -0.3 || -0.2 || -0.3 || -0.1 || -0.1 || -0.2 || -0.2 || -0.3 || -0.2 || -0.2 || -0.2

CAN || -0.1 || 0.0 || 0.0 || -0.2 || -0.1 || -0.1 || -0.1 || -0.3 || -0.1 || -0.1 || -0.1 || -0.3 || -0.1 || -0.1 || -0.1 || -0.6

CHN || 0.7 || 0.7 || 0.3 || 0.9 || 1.0 || 0.9 || 1.0 || 1.3 || 4.0 || 1.4 || 1.5 || 1.0 || 1.1 || 1.0 || 0.6 || 1.5

EU-12 || 1.7 || 0.1 || 0.0 || 0.0 || 4.8 || -0.1 || 0.0 || -0.1 || 2.5 || 0.1 || 0.0 || -0.1 || 4.9 || 0.0 || 0.0 || -0.1

EU-15 || -0.9 || 0.0 || -0.1 || -0.1 || -3.4 || 1.0 || -0.6 || -1.0 || -3.7 || 0.5 || -0.5 || -0.9 || -4.1 || 0.5 || -0.8 || -0.8

JPN || -0.1 || -0.1 || 1.8 || -0.1 || -0.2 || -0.2 || 1.6 || -0.3 || -0.6 || -0.3 || 1.3 || -0.5 || -0.3 || -0.2 || 2.0 || -0.5

KOR || -0.1 || 0.0 || -0.2 || -0.1 || 0.0 || -0.1 || -0.3 || -0.1 || -0.2 || -0.2 || -0.4 || -0.3 || -0.1 || 0.1 || -0.2 || -0.1

MEX || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || 0.0 || -0.3 || 0.0 || -0.1 || 0.0 || 0.0

USA || 0.0 || 0.2 || -0.2 || 0.0 || -0.1 || -0.1 || -0.4 || 1.9 || -0.2 || -0.2 || -0.4 || 3.2 || -0.1 || -0.2 || -0.4 || 1.6

ROW || -0.9 || -0.5 || -1.4 || -0.4 || -1.5 || -1.1 || -1.0 || -1.1 || -1.6 || -1.1 || -1.1 || -1.6 || -0.9 || -0.9 || -0.8 || -0.8

Note: BRII comprises Brazil, Russia, India and
Indonesia.

Source: WIOD.

              2.3. Effects of the crisis on trade and international supply chains

This section
analyses the effects of the 2008 trade slump on EU-27 trade structures,
compared to other major economies such as the US and Japan. Of particular
interest is whether the geographical sourcing patterns by industry are
different to those before the crisis. The analysis allows an assessment to be
made as to whether the crisis has led to a change in the structure of vertical
specialisation in this respect. Particular attention is paid to international
supply structures with respect to traded intermediates, and in particular
semi-finished products and parts and components in the industries concerned.

The analysis
will be based on the UN Comtrade data, providing exports and imports at the HS
6-digit level, which allows for differentiation by broad end-use categories
(BEC) and NACE industries. The time period covered is 2005–10.
Methodologically, the study builds on recent attempts to decompose the trade
slump (see e.g. Aurújo, 2009; Haddad et al., 2010; Levchenko and Lewis,
2009).

              2.3.1 Geographical evolution of trade structures during the crisis

While the
crisis had a major impact on all major economies, the more rapid recovery of
countries such as China has had an impact on its main trading partners, e.g. Japan. Figure 2.4 presents data on changes in the imports of the EU-12, the EU-15, Japan and the US, by trading partner, as a percentage of total trade in 2007. It is immediately
apparent that the ‘Chemicals’ and ‘Electrical and optical equipment’
industries have recovered faster than the other two industries. In all of the
advanced economies considered, imports in the chemical industries in 2010
reached or surpassed 2007 levels. Japan, in particular, increased its imports
dramatically, with those from the EU-15 rising by 34 % and from the US by 25 % relative to the initial trade values with these partners. Imports from the
EU-15 and EU-12 rose in all the economies considered — with the exception of
the EU-15 itself.

The ‘Electrical
and optical equipment’ industry provides the most striking example of rising
imports from China. Not only have exports to China increased for almost all
reporters and industries, but so have imports from China. This is exceptional,
given the economic crisis. Relative to imports from China in 2007, they have
increased by 59 % for the EU-12, 19 % for the EU-15, 39 % for Japan and 25 % for the US. Imports from the EU-12 have also risen quite substantially for all
reporting countries. While the EU-12 is not a major trading partner of Japan and the US, and import levels are therefore quite low, intra-EU-12 trade increased by 30 %
and imports from the EU-15 by 24 % (see Stehrer et al. 2012b for details).

The two
industries ‘Machinery and equipment’ and ‘Transport equipment’ are both
characterised by a sharp decline in imports from the EU-15, Japan and the US. Imports from the EU-15 decreased in most countries by more than 20 %. This
has had a large impact on the total imports in these industries as the EU-15 is
a major trading partner of all the reporters considered. In relative terms,
most of the other major advanced economies did not perform any better. Imports
from Japan decreased by 25–28 % for ‘Transport equipment’, and Japanese imports
from the US also plummeted by 25–28 %. On the other hand, transatlantic
linkages remained comparatively stable, as EU-27 imports from the US only declined by 11–16 %.

Overall,
imports from China rose in all major economies during this period. Firms
maintained their sourcing connections with China, even though imports from
almost all other major trading partners fell. These findings are in line with
the results of the analyses in the previous section, which showed that China is essentially the only country with growing shares in extra sourcing.

Figure 2.4 – Changes
in imports (2007–10) of total imports in 2007(%)

Source: UN Comtrade; authors’
calculations.

              2.3.2 Decomposition of  trade by product usage

This section
presents a more in-depth analysis of trade during the crisis by adding another
layer. By decomposing the imports of an industry into trade in parts and
components, semi-finished products, consumption and capital goods, it is
possible to take a detailed look at vertical changes in trade. Figure 2.5
provides an overview of the import composition of each industry. Trade in parts
and components constitutes a major part of total trade in the ‘Machinery and
equipment’, ‘Electrical and optical equipment’ and ‘Transport equipment’
industries. Particularly in ‘Machinery and equipment’, the trade in parts and
components was growing strongly before the crisis, with an annual rate of 19 %,
exceeding the growth rate in consumption goods (9 %) and capital goods (16 %).Trade
in parts and components does not play a role in the chemical industry, where
semi-finished products are the dominant trade element, comprising 67 % of
total imports.

The
composition looks similar for EU-27 exports, albeit with slightly lower shares
of capital and consumption goods.

Figure 2.5 – Decomposition of
EU-27 imports, by use categories, 2010

Source: UN Comtrade; authors’
calculations.

Figure 2.6
shows the development of EU-27 imports by use categories. In most industries,
there is a sharper decline in imports of semi-finished products and parts and
components than in imports of consumption goods. There are two reasons
for this strong decrease in intermediate products. The first is that, as
countries become more vertically specialised, the processing of a product at
various production stages tends to involve a number of countries. For this
reason, trade declines not only by the value of the finished products which are
exported, but also by the value of all the intermediate trade flows that have
been traded to create it (see also Yi, 2009; Bergoeing et al., 2004).

Figure 2.6 –
Development of EU-27 imports, by use categories (2008=100)

Source: UN Comtrade; authors’ calculations.

Inventory
management of firms is another reason for the downturn in trade in intermediate
products during crisis periods, (Alessandria et al., 2011). As a reaction to the
demand shock, retailers and manufacturers not only reduce their orders by the
amount of the demand shock, but also reduce their inventories. This decrease in
inventories can be seen in aggregate statistics over the recent crisis. Each
supplier faces not only the demand shock from the customer, but also the
inventory effect at each production stage. The effect is thus
aggravated as one moves up the supply chain, from end consumer to raw
material supplier (Altomonte et al., 2011). The more complex the supply chains
and the more they are spread across countries, the more noticeable is this
so-called ‘bullwhip’ or ‘Forrester effect’ (Forrester, 1961) in international
trade patterns. The decline in intermediates in ‘Transport equipment’ has not
been quite as big as for consumer goods. This is partly explained by
‘just-in-time’ production, which leads to minimal inventories and therefore a
small bullwhip effect.

Finally,
EU-27 trade is analysed with respect to the partner countries and use category.
Trends before the crisis (2005–07) are compared with those during the crisis
(2008–10). To do this, annual changes in imports in the EU-27 are calculated
for each industry, use category and partner (Table 2.8).

Before the
crisis, EU-27 imports of semi-finished chemical products from advanced
countries increased much faster than imports of consumer goods. The opposite is
true of trade with the EU-12, where trade in consumer goods increased most.
This indicates that the EU-12 countries strengthened their position as a final
producer of chemical products.

‘Machinery
and equipment’ registered the strongest growth rates in imports of parts and components.
The annual growth in EU-27 imports between 2005 and 2007 is impressive: 62 %
for China, 47 % for Japan, 43 % for Korea, 26 % for the EU-12
and 20 % for the EU-15. The role of the US in the EU-27 production
networks has been decreasing, relatively speaking, as imports of parts and
components grew by ‘only’ 10 %. During the crisis, imports of parts and
components and semi-finished products fell more than imports of consumption
goods. Also, the trade in capital goods dropped significantly as firms extended
their investments. On the geographical front, it is clear that there was a
similar fall in imports in the EU-27, the US and Japan (mostly between 10 %
and 20 %), while imports from China increased slightly overall.

Table 2.8 – EU-27 imports by partner, industry and use category: import
share of partner in 2007, annual growth 2005–07 and 2008–10 ( %)

|| || Partner

NACE || Use category || EU-12 || EU-15 || JPN || USA || BRII || CHN || KOR || RoW

|| || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10 || 2007 || 05- 07 || 08- 10

|| Chemicals || || || || || || || || || || || || || || || || || || || || || || || ||

|| Consumption (33%) || 3.3 || 30 || 10 || 76.4 || 11 || -6 || 1.1 || 5 || -8 || 8.2 || 8 || 6 || 0.5 || 10 || 24 || 1.1 || 25 || 2 || 0.3 || 59 || -50 || 9.0 || 16 || 5

|| Semi-finished (67%) || 3.4 || 21 || -7 || 67.0 || 14 || -5 || 2.1 || 10 || -3 || 9.0 || 15 || -4 || 2.9 || 27 || -5 || 2.1 || 23 || 4 || 0.5 || 18 || 0 || 13.0 || 20 || 0

|| Machinery and equipment || || || || || || || || || || || || || || || || || || || || || || || ||

|| Capital goods (45%) || 5.2 || 26 || -8 || 63.6 || 19 || -21 || 6.3 || 14 || -21 || 5.6 || 15 || -18 || 0.8 || 28 || -16 || 7.1 || 46 || -4 || 1.5 || 30 || -26 || 10.0 || 22 || -18

|| Consumption (10%) || 14.4 || 22 || -2 || 48.1 || 6 || -10 || 1.5 || -1 || -6 || 3.1 || 9 || -13 || 0.3 || -6 || 17 || 20.9 || 16 || 5 || 2.2 || 3 || 1 || 9.5 || 18 || -4

|| Parts and components (44%) || 8.8 || 26 || -15 || 60.0 || 20 || -15 || 6.7 || 47 || -12 || 7.3 || 10 || -8 || 1.2 || 29 || -14 || 5.7 || 62 || 0 || 0.9 || 43 || -11 || 9.4 || 28 || -6

|| Semi-finished (1%) || 15.5 || 19 || -17 || 55.0 || 11 || -10 || 2.4 || 12 || 0 || 2.9 || 13 || -12 || 1.3 || 16 || -22 || 13.3 || 22 || 4 || 0.4 || 0 || 7 || 9.2 || 17 || -2

|| Electrical and optical eqpt. || || || || || || || || || || || || || || || || || || || || || || || ||

|| Capital goods (46%) || 6.8 || 11 || 4 || 42.8 || 2 || -9 || 4.5 || -8 || -6 || 10.5 || 12 || -11 || 1.0 || 15 || 1 || 19.6 || 11 || 7 || 3.2 || -11 || -22 || 11.6 || 0 || -1

|| Consumption (11%) || 18.9 || 47 || 4 || 34.9 || 10 || -9 || 3.0 || 3 || -10 || 6.2 || 6 || 2 || 0.6 || 0 || -15 || 17.6 || 26 || -3 || 2.8 || 34 || -11 || 15.9 || 7 || -1

|| Parts and components (35%) || 7.0 || 19 || -2 || 42.2 || 6 || -6 || 5.5 || -8 || -17 || 7.7 || -2 || -9 || 0.8 || 18 || -2 || 13.5 || 20 || 10 || 4.6 || 23 || 10 || 18.6 || 7 || -2

|| Semi-finished (8%) || 17.7 || 21 || -4 || 47.8 || 17 || -8 || 2.8 || 23 || -1 || 3.4 || 11 || -3 || 1.1 || 24 || -7 || 12.8 || 24 || 3 || 1.1 || 46 || 10 || 13.2 || 22 || 1

|| Transport equipment || || || || || || || || || || || || || || || || || || || || || || || ||

|| Capital goods (20%) || 4.7 || 30 || -15 || 67.7 || 18 || -13 || 1.5 || 23 || -12 || 10.7 || -5 || -20 || 1.1 || 29 || 28 || 1.5 || 23 || 46 || 2.3 || -1 || 16 || 10.5 || 1 || -12

|| Consumption (39%) || 9.4 || 34 || 0 || 71.2 || 10 || -14 || 7.6 || 7 || -21 || 3.5 || 29 || -32 || 0.5 || 13 || 6 || 0.5 || 26 || -9 || 3.1 || 4 || -26 || 4.2 || 19 || -3

|| Parts and components (41%) || 12.8 || 20 || -4 || 66.7 || 13 || -11 || 3.2 || 9 || -6 || 8.4 || 10 || -1 || 1.1 || 16 || -10 || 1.3 || 26 || 6 || 0.6 || 48 || 11 || 6.0 || 18 || -7

Notes: The first (grey) column for each country is the
share of this partner in EU-27 imports in this category in 2007. The second
column is the annual growth rate in 2005–07 and the third column is the growth
rate for 2008–10.

Source: UN Comtrade; authors’
calculations.

‘Transport equipment’ registered a significant
drop in imports of consumption goods from the US (-32 %), Japan (-21 %) and Korea (-26 %) — far greater than intra-EU-27 changes (-12 %). On
the other hand, overseas production network linkages remained fairly stable or
were further strengthened, as in the case of China and Korea, while imports of parts and components from the EU-15 dropped by 11 %.

Finally, Japan’s traditional image as a prominent player in the ‘Electrical and optical equipment’
market seems to be starting to crumble. Even before the crisis, EU-27 imports
of capital goods and parts and components were falling by 8 % on an annual
basis. This trend continued during the crisis, with the largest drop in parts
and components trade (17 %). By contrast, the importance of the EU-12, China and Korea increased significantly before the crisis, and China and Korea even increased their
trade levels during the crisis in capital and parts and components. China’s role as an assembly country and provider of consumption goods has decreased in very
recent years, whereas its direct integration into production networks as a
provider of parts and components has increased.

              2.4. Off-shoring decisions of EU
manufacturing firms

This
section analyses the decision by European manufacturing firms to move their production
to locations abroad (referred to as offshoring). There is a strong relationship
between offshoring and the trade in intermediates, analysed in the previous
section. If firms move production activities to their own or independent firms
abroad, this will inevitably increase the imports of intermediates. However,
offshoring may also go beyond a simple substitution of domestic production by
imports. If new production facilities abroad have larger capacity than the
previous activities at home, this can lead to positive ‘second-round effects’
(when the new locations need a higher amount of input or support from the home
base). Offshoring is not only a strategy to cut costs, but is also driven by
the need to open up new markets and to operate close to key clients.

Against this
background, this section investigates the following questions: Which types of
European manufacturing firms offshore their production activities? What are the
main destination countries for offshoring? How is offshoring related to innovation
and company performance? What are the short-term and long-term trends in
offshoring? Has the 2008/09 economic crisis altered or even halted the trend
towards stronger fragmentation of firms’ global production chains? Or, on the
contrary, have companies become more active again so as to better control their
cost base at a time when production volumes are falling?

The data come
from the European Manufacturing Survey (EMS), a survey of product, process, service and organisational
innovation in European manufacturing. EMS data are available for the two
periods mid-2004 to mid-2006 and 2007 to mid-2009. The sample includes
firms from the four industrial sectors;  they are studied in more detail below.

              2.4.1 Which firms offshore?

Around 20 %
of all firms in the four manufacturing sectors, covered by the 2009 survey,
moved part of their production offshore to their own or independent firms
abroad in the period from 2007 to mid-2009. Germany, the largest country in the
sample, has a share of offshoring firms of around 16 % in the four
manufacturing sectors mentioned above.

If the two
periods — mid-2004 to mid-2006 and 2007 to mid-2009 — are compared, six out of
seven countries show a decrease in the proportion of firms with offshore
production. Manufacturing firms were less inclined to offshore during the
crisis of 2008/09. European manufacturing companies tended to maintain
production at home and make use of the capacity at their existing locations,
rather than look for new offshoring ventures.

Production
offshoring is a strategy favoured by large firms in particular (see Figure
2.7). In 2007-2009 some 41 % of the firms with more than 250 employees
relocated parts of their production abroad, whereas the corresponding share
among small firms of less than 50 employees was only 8 %. During the
crisis, offshoring decreased in all firm size categories.

Figure
2.7 – Share of firms with production offshoring, by size category

Source: European
Manufacturing Survey 2006, 2009.

Firms in the
electrical and optical equipment industry and automotive and transport
equipment manufacturers are particularly active in production relocation (25 %
and 24 % respectively), followed by machinery and equipment manufacturers
(18 %) and the chemical industry (14 %). The chemical industry has
traditionally been quite reserved about production relocation, due to the high
capital intensity, the high degree of process integration and the low labour
intensity of its production processes. As in the case of the different sizes of
firms, offshoring is decreasing in all four sectors.

2.4.2 Offshoring
motives and destinations

According to
the data, cost reduction is the dominant motive for relocating production activities
abroad: 72 % of all firms with offshoring activities stated that labour
costs had triggered their offshoring decision. Compared to the previous survey,
the importance of labour costs decreased slightly (by 4 percentage points)
(Figure 2.8).

Market-related
motives, such as proximity to customers or market expansion, gained far fewer
votes. The least relevant motives for production offshoring were better access
to knowledge, and taxes and subsidies in the target country.

Figure
2.8 – Main motives for production relocations

Note: Multiple answers allowed.

Source: European
Manufacturing Survey 2006, 2009.

Besides the
all-important consideration of labour cost savings, there are usually a host of
factors that make locations attractive as destinations for production offshoring.
This is reflected in the high number of multiple answers, as shown in Figure
2.8. Besides cutting costs, production offshoring also has the goal of
expanding activities and opening up new markets; this is reflected in the
proportion of motives related to expansion of markets and proximity to key
customers abroad (which has gained importance since the previous survey).

There is also
a strong link between motives and choice of destination country for production
offshoring. Regression analysis indicates that when companies are striving to
reduce labour costs, the EU-12, China and other Asian countries are the
preferred target regions. The main difference between Asian countries and the
EU-12 is that the labour cost motive is linked to the market expansion motive
in the case of Asian countries, but not in the case of the EU-12. The fact that
markets in the EU-12 and Eastern Europe can more easily be supplied with
exports from the home country might account for the lack of market and customer
incentives in these countries.

Low
transportation costs and access to knowledge, by contrast, are motives related
to offshoring to the EU-15. Offshoring to North America is significantly
related to the need to be close to important customers.

The EU-12
Member States are the preferred target region for production relocations,
accounting for 30% of all valid responses from offshoring companies (Figure
2.9). Compared to the previous period (mid-2004 to mid-2006) their share
dropped by 7 percentage points.

China is the
second most attractive destination, accounting for 28 % of all valid
answers in 2009. In contrast to the EU-12, China has become more attractive
than before. In particular small and medium-sized companies intensified their
production relocation to China (from 6 % and 15 %, respectively, to
20 % and 33 % of all offshoring firms). It should be noted, however,
that the share of firms that moved production offshore to China remained virtually unchanged if one looks at the whole sample rather than just the offshoring
firms, because the overall propensity to offshore has declined. Relocations to
the EU-15 Member States remained stable, at around 13 % of all offshoring
firms. The EU-15 countries are still the third most attractive region for
relocation for European manufacturing companies. They are followed by other
Asian countries excluding China (10 %) and non-EU Eastern Europe (8 %).

Overall, it
can be concluded that farshoring to Asian countries has gained in
attractiveness for offshoring firms, while nearshoring to the EU-12
countries has decreased noticeably. As a result, production relocation between
EU Member States (intra-EU-27) is decreasing while extra-EU-27
relocation activities have gained ground.

Figure
2.9 – Target regions of production offshoring, only offshoring firms

Note: Multiple answers allowed.

Source: European
Manufacturing Survey 2006, 2009.

2.4.3
Characteristics of offshoring firms

The empirical
evidence presented above indicates that firm size, sector and location of the
firm strongly determine offshoring decisions. These determinants have been
analysed further using multivariate analysis to gain a better understanding of
which firms offshore and which do not.

The analysis
shows the relationship between the decision to offshore and each explanatory
variable included in the regression analysis, holding all other explanatory
variables constant. The dependent variable of the analysis is a dummy variable
that is one if the firm offshored production activities to its own or
independent firms between 2006 and 2009.

Explanatory
variables include first a number of variables that describe firm
characteristics, including firm size, revenue per employee as
a measure of productivity, the share of exports on turnover or a
dummy variable that is one if the firm is a supplier of intermediary goods.
Based on the literature, larger, more productive firms are assumed have a
higher propensity to move their production activities abroad. Moreover, an
intermediate supplier may feel compelled to follow customers who move their
production activities offshore.

A second set
of explanatory variables describes the innovation behaviour of the firm.
These variables include R&D expenditure as a share of turnover, a
dummy variable that is one if the firm has introduced a product innovation
in the period 2006-2008, and the share of new products on turnover. If
more productive firms have a higher propensity to offshore, then they may also
be more innovation active. Moreover, offshoring of production may lead to a new
division of labour within the firm, where the parent company focuses on activities
such as R&D, innovation and marketing.

A third set
of variables describe the production process of the firm. Two dummy variables
indicate whether the firm produces simple or complex products
consisting of many parts. The baseline case for both variables is medium
complex products. Two other dummy variables show whether the firm produces single
units or in large batches. Here, the baseline case is small batches.
Moreover, three dummies are included that gauge the degree of
standardisation in product development. It is assumed that firms that
produce complex, highly-customised products in single production unit may have
less opportunity to offshore because they rely very much on a close interaction
with the customer, and are therefore more bound to their location than
producers of standardised goods in large batches.

Finally, the
regression includes explanatory variables that control for the sector and
the location of the firm to test if the differences in the offshoring
propensity across sectors and countries can be explained by the firm
characteristics listed above. The regression also tests the assumption that the
degree of product market regulation in a country is related to offshoring, i.e.
firms relocate production because of too much regulation. The variable product
market regulation provided by the OECD has been introduced into the
regression. This variable captures various aspects of regulation, such as
barriers to trade and investment, state control or barriers to
entrepreneurship, in one single number for each country.

A probit
regression model is estimated to analyse the linkages between firm
characteristics and the manufacturing firm’s probability of offshoring
production activities. The probit model is given as

where Y\*
can be viewed as an indicator for whether the latent dependent variable Y
– the probability of offshoring – is positive

with X’
denoting the vector of binary explanatory variables and β being the
parameter reflecting the marginal effect of a discrete change in the
probability to offshore for the explanatory variables. Ε is the error
term, which is assumed to be of zero mean and with a standard deviation of
σ2.

The results
are presented in Table 2.9 which shows the results from the analysis of factors
determining outshoring decisions between 2006 and 2009. The first three columns
include dummy variables controlling for firms' home countries. The right three columns
contains results from controlling for the degree of product market regulation
in home countries.

The results
confirm a positive relationship between firm size and offshoring, holding all
other factors constant. If two firms are the same in all variables employed in
the regression except for size, the larger firm will, on average, have a higher
propensity to offshore. A similar positive relationship is also found for
revenue per employee and offshoring.

The
relationship between innovation and offshoring is not clear cut. Offshoring
firms, on the one hand, spend slightly less on R&D than non-offshoring
firms; on the other hand, they introduce new products onto the market
significantly more often. This result points to the fact that offshoring is not
only a passive reaction to rising wage costs, but has to be seen in the wider
context of the international expansion of firms. Offshoring firms are
also characterised by the development and production of a standard programme of
less complex products.

The results
clearly show that there is a strong relationship between the firm’s sector
affiliation and the probability that it will offshore production abroad. Firms
that belong to the machinery and equipment, electrical and optical equipment,
and transport equipment sectors show a higher propensity to offshore than those
in the sector of chemicals and chemical products.

Moreover, the
results confirm that not only do sector and firm size explain the propensity to
offshore to a larger degree than firms’ characteristics, but so does the firm’s
home country. Being a Dutch or a Swiss firm has a significant positive effect
on offshoring, compared to being a German firm. Austrian, Danish, Finnish,
Spanish and Slovenian/Croatian firms do not differ significantly from German
firms in their propensity to offshore.

The
regression also tests the assumption that the degree of product market
regulation in a country is related to offshoring, i.e. firms relocate
production because of too much regulation. The analysis does not support this
assumption.

Table 2.9 –
Probit regression on the probability of being an offshoring firm, 2006–2009

|| 2006 || || 2009

Propensity to offshore production || Coefficient || Sig. || Std.err. || || Coefficient || Sig. || Std.err.

General || || || || || || ||

Size (log function of number of employees) || 0.101 || \*\*\* || 0.007 || || 0.094 || \*\*\* || 0.007

log revenue per employee || 0.041 || \*\*\* || 0.015 || || 0.050 || \*\*\* || 0.016

Export share (% of turnover) || 0.001 || \*\*\* || 0.000 || || 0.001 || \*\*\* || 0.000

Intermediate supplier\* || -0.037 || \* || 0.019 || || -0.035 || \* || 0.020

Innovation || || || || || || ||

Share of R&D expenditure (% of turnover) || -0.004 || \*\* || 0.002 || || -0.005 || \*\*\* || 0.002

Product innovator (new to firm innovation)\* || 0.053 || \*\* || 0.021 || || 0.050 || \*\* || 0.022

Share of product innovations (% of turnover) || -0.001 || \*\* || 0.001 || || -0.001 || \* || 0.001

Product complexity (a) || || || || || || ||

Simple products\* || 0.035 || || 0.037 || || 0.040 || || 0.038

Complex products\* || -0.046 || \*\* || 0.020 || || -0.044 || \*\* || 0.020

Batch size (b) || || || || || || ||

Single unit production\* || -0.020 || || 0.022 || || -0.032 || || 0.022

Large batch\* || 0.068 || \*\* || 0.029 || || 0.040 || || 0.029

Product development (c) || || || || || || ||

According to customers’ specification\* || -0.007 || || 0.020 || || -0.009 || || 0.020

Standard programme\* || 0.064 || \*\* || 0.031 || || 0.064 || \*\* || 0.031

No product development\* || -0.069 || || 0.039 || || -0.088 || \*\*\* || 0.038

Sector (d) || || || || || || ||

Machinery and equipment\* || 0.169 || \*\*\* || 0.037 || || 0.161 || \*\*\* || 0.037

Electrical and optical equipment\* || 0.224 || \*\*\* || 0.039 || || 0.216 || \*\*\* || 0.039

Transport equipment\* || 0.178 || \*\*\* || 0.055 || || 0.154 || \*\*\* || 0.056

Country (e) || || || || || || ||

AT\* || 0.031 || || 0.037 || || || ||

CH\* || 0.064 || \*\*\* || 0.025 || || || ||

NL\* || 0.142 || \*\*\* || 0.046 || || || ||

DK\* || 0.088 || || 0.072 || || || ||

HR & SI\* || -0.057 || || 0.038 || || || ||

FI\* || 0.033 || || 0.074 || || || ||

ES\* || -0.033 || || 0.046 || || || ||

Product market regulation || || || || || -0.071 || || 0.046

Sample size || 2,476 || || || || 2,359 || ||

Pseudo R2 || 0.1502 || || || || 0.1416 || ||

Note: (\*) dF/dx is for discrete change of dummy
variable from 0 to 1. Reference groups: (a) medium complexity, (b)
medium batch, (c) basic programme with alternative, (d)
chemicals and chemical products, (e) Germany. Difference in means of
the independent variables significantly diverge from zero, probability values
of 10% (\*), 5% (\*\*) or 1% (\*\*\*).

Source: European
Manufacturing Survey 2006, 2009.

              2.5. Summary and policy implications

The study
provides an overview of the tendencies observed in the internationalisation of
production since 1995 and over the period of the recent crisis. As outlined
above, there is no single approach that allows the many facets of this phenomenon
to be captured at the various levels of aggregation: from single-firm decisions
to overall industry-level patterns and macroeconomic consequences. Therefore,
various approaches have been used here to analyse this internationalisation
process, in order to highlight some of the main aspects. Based on the recently
compiled world input-output tables from the WIOD project, ongoing trends in the
vertical specialisation patterns for the EU countries and other major economies
have been documented. Generally, one finds that, for the EU, the integration
process since 1995 has intensified the internationalisation of production
within Europe considerably — and the  EU-12 countries play a particular role in
this respect. But the rise of China as a major partner is also well documented
in this exercise. An important finding is that during the recent crisis there
was a tendency towards less integration, which manifested itself in the
resurgence of domestic rather than foreign sourcing. The only foreign country
that has continued to increase its share in the EU sourcing structures has been
China. Although this phenomenon of ‘backshoring’ might be caused by those
industries that have been most affected by the crisis, it might also be
indicative of a rupture in the trend towards more offshoring and ‘farshoring’.
Albeit to varying degrees, the trends seem to be similar for all four sectors
that have been studied in more detail.

The economic
and financial crisis that broke out in 2008 was accompanied by a great fall in
foreign trade volumes. The extent of the trade collapse was greater than the
decline in output. Thus international trade can be regarded as one of the great
‘victims’ of the world crisis. At the same time, it was also one of the
channels through which the crisis was transmitted between countries. It seems
that production chains in the first phase of the crisis had an amplifying
effect in terms of the decrease in international trade, which is referred to as
the ‘bullwhip effect’. On the other hand, there is a certain stabilising effect
created by value chains, at least in the slightly longer run. This may be
caused by the reversal of the bullwhip effect, as well as by the fact that
companies inside the value chain helped each other, e.g. by providing trade
finance. With regard to the changing role of the internationalisation of
production as a result of the crisis, it is obvious that the
internationalisation of production is here to stay.

The focus on
industry-level data brought about by using trade statistics, or trade statistics
combined with detailed input-output tables, might hide aspects of this
internationalisation process that can only be seen at the level of firms. The
last section investigated offshoring — the relocation of production activities
to locations abroad — by European firms. The analyses show that the share of
offshoring firms decreased across most countries, sectors and firm sizes
between the periods 2004-06 and 2007-09. This may indicate that firms focus on
utilising their activities at home in times of (upcoming) economic crisis.

The main
target regions for offshoring by European firms are the EU-12, China, the EU-15 and other Asian locations excluding China. Despite a general decrease in the share
of offshoring firms, farshoring to Asia and China, in particular, has increased. By contrast, nearshoring to the EU-12 has
become less attractive, though it is still the most important target region. An
explanation for this shift may be an increase in labour costs in the EU-12
countries, coupled with their geographical proximity, which allows firms to
serve these markets from their home countries.

The dominant
motive for production offshoring is the desire to reduce labour costs, followed
(at some considerable distance) by proximity to customers and market expansion.
Expected labour cost reductions explain offshoring to the EU-12, Asia and China, in particular. However, in contrast to the EU-12, where the offshoring decision is
dominated solely by potential labour cost savings, customer and market
expansion motives are also significantly related to offshoring activities
involving Asia and China.

Characteristics
of firms that have offshored production activities include larger firm size and
greater revenue per employee, a standard programme of less complex products,
and a higher probability of introducing new products to the market. Producers
of electrical and optical equipment have a higher propensity to offshore
production than do firms in the other three sectors considered. Previous
experience of production offshoring goes a long way towards determining
production offshoring today. Product market regulation does not seem to be a
push factor for firms to offshore production activities abroad.

The
increasing use of foreign sourcing for the content of exports in the
manufacturing industries illustrates well how globalisation has impacted firms’
value chains. The increased pace of globalisation has improved firms’ and
industries’ opportunities to source inputs and intermediates from locations
which have comparative advantages in producing these inputs and intermediates
which is now better reflected in different parts of firms’ value chains. The
higher use of foreign content by industries that are more highly dependent on
intermediates clearly shows that this is key for competitiveness.

The
globalisation of value chains gives rise to some policy challenges due to the
new opportunities and challenges which the increased globalisation leads to.
Some of these policy challenges are already familiar to some extent and relate
to policies aimed at reaping the benefits of openness for trade and FDI.

The growing
importance of intermediate goods for exports and competitiveness of firms
illustrates that the costs of national borders have grown as trade costs are
more important for intra-firm and vertical trade within global value chains
(GVCs) compared to traditional trade where intermediates and inputs are
produced domestically. Raising barriers to international trade and direct
investments can therefore disrupt GVCs for domestic firms that source
intermediates from abroad. As pointed out in the Communication ‘Trade, Growth
and World Affairs’ and the associated Staff Working Document ‘Trade as a driver
of prosperity’, openness to trade facilitates local companies’ integration in GVCs
which makes them more productive. And more than two thirds of EU imports
consist of intermediate products which boost EU industry productivity.[40]

Multinational
enterprises have been driving the emergence of GVCs through intra-firm trade
and FDI flows. In order to reap the benefits of globalisation and GVCs on a
broader scale, participation in GVCs, particularly of SMEs, needs to increase.
In many cases, SMEs lack the expertise and capacity to engage in international
trade directly; more opportunities for creating or strengthening linkages
between local firms and firms that are already engaged in GVCs would be
beneficial.

The emergence
of GVCs and increased participation of countries also give rise to challenges.
As is well established, most of the value is created in the upper and lower
part of the value chains where activities such as R&D, branding, design,
management, marketing and sales services are located. While emerging countries
formulate policies on how to move up the value chain, policies to keep the
comparative advantage in high value-added activities are more relevant for the
EU. Intangible assets are crucial in this respect. Investments in intangibles
are essential for innovation and important for capturing larger shares of value
in the value chains. Investments in intangibles enable firms to create superior
capabilities which help them acquire unique skills or suppliers of unique
factors indispensable to the whole value chain. Firms that possess such unique,
idiosyncratic, specific factors in the GVC capture the largest shares of
value-added. Innovation is the most important source for capturing value-added
and developing or keeping competitive advantages. The oft-cited examples of the
Nokia 95 model and the iPhone illustrate that the locational advantages of
the home countries for activities in the upper part of the value chains relate
to their attractiveness for innovation and the development of intangible
assets. Innovation policies are therefore obvious candidates. But consideration
should also be given to policies that help localise factors that are essential
for activities which capture large shares of value-added.

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              Annex 1. The World Input-Output Database (WIOD)

Box 2.1 – The World Input-Output Database
(WIOD)[41]

The
data used are taken from the World Input-Output Database (WIOD), which became
available in April 2012 (see www.wiod.org)
and was compiled within the EU Framework programme. These data provide
international supply and use and input-output tables for a set of 41 countries
(the EU-27, Australia, Brazil, Canada, China, India, Indonesia, Japan, Korea, Mexico, Russia, Taiwan, Turkey, the US and the Rest of World) over the period
1995–2009. It was compiled on the basis of national accounts, national supply
and use tables and detailed trade data on goods and services, combining
information for 59 products and 35 industries. Corresponding data at the
industry level allow the splitting up of value-added into capital and labour
income. For detailed information see Timmer et al. (2012).

This
results in a world input-output database for 41 (including the Rest of World)
countries and 35 industries, i.e. the intermediates demand block is of the
dimension 1 435x1 435, plus additional rows on value-added and
columns on final demand categories. The outline of such a world input-output
table is presented below. Each country listed vertically sources intermediates
from its own industries and from other countries’ industries. Together with
value-added from this country, the level of gross output is obtained.
Furthermore, each country also demands products from its own economy and the
other economies for final use, such as consumption and gross fixed capital
formation. The horizontal view shows what each country’s industries provide to
industries in its own country and the other countries, and as final demand for
domestic and foreign consumers. Gross output produced in one country equals the
value of demand for each country’s industries.

Outline of world input-output table (industry by
industry)

              Annex 2. The European manufacturing survey

The European
Manufacturing Survey (EMS) investigates technological and non-technological
innovation in European industry. It focuses on fields such as technical
modernisation of value-adding processes, the introduction of innovative
organisational concepts, including international offshoring and outsourcing of
production and R&D activities, and new business models for complementing
the product portfolio with innovative services. The questions on these
indicators have been agreed upon in the EMS consortium and are surveyed in all
the participating countries. Additionally, some countries ask questions on
specific topics. The underlying idea of the question design is to have a common
core of questions asked consistently over several survey rounds; to modify
other common questions in a survey round in order to correspond to actual
trends, problems and topics; and to provide space for some country- or
project-specific topics.

In most
countries, EMS is carried out as a paper-based survey at company level. In
order to prepare for multinational analyses, the national data undergo a joint
harmonisation procedure.

The latest
survey – EMS 2009 – was carried out in 13 countries. Information on the
utilisation of innovative organisation and technology concepts in the
generation of products and services, as well as performance indicators such as
productivity, flexibility and quality was collected for more than 3,500
companies from the manufacturing sector in these countries.

The dataset
employed in this report was compiled using those country surveys that included
questions on the companies’ production relocation behaviour, conducted in nine
European countries. It includes the Austrian, Croatian, German, Dutch,
Slovenian, Spanish and Swiss datasets collected in 2009 and 2006. The Danish
and Finnish datasets are only available for the 2009 round, as the respective
partners joined the EMS network after 2006. While most partners sent out their
questionnaires by mail, the Finnish and Danish data were collected using an
online questionnaire. Those asked to fill in the questionnaires were the
production managers or CEOs of the manufacturing firms contacted.

This report
focuses on actual trends and developments in production relocation activities
of European manufacturing companies in the following industrial sectors:
chemicals/chemical products (NACE 24), machinery and equipment (NACE 29),
electrical and optical equipment (NACE 30–33) and transport equipment (NACE
34–35).

Table A.2.1
below provides an overview of the sample, broken down by sector, firm size and
country distribution for the EMS surveys 2006 and 2009.

Table A.2.1 – Sample of surveyed firms, by firm
size, country and sector, 2006 and 2009

|| 2006 || 2009

Firm size || N || % || N || %

Up to 49 50 to 249 250 and more || 435 669 348 || 29.96 46.07 23.97 || 476 663 288 || 33.36 46.46 20.18

Sector || N || % || N || %

Chemicals/chemical products (a) Machinery & equipment (b) Electrical & optical equipment (c) Transport equipment (d) || 170 617 537 128 || 11.71 42.49 36.98 8.82 || 180 628 507 112 || 12.61 44.01 35.53 7.85

Country || N || % || N || %

Germany Austria Switzerland Netherlands Denmark Croatia Finland Spain Slovenia || 847 89 299 89 40 56 32 || 58.33 6.13 20.59 6.13 2.75 3.86 2.2 || 635 102 303 116 143 24 42 32 30 || 44.5 7.15 21.23 8.13 10.02 1.68 2.94 2.24 2.1

Total || 1452 || || 1427 ||

Note: (a)
NACE 24, (b) NACE 29, (c) NACE 30–33, (d) NACE 34–35.

Source: European
Manufacturing Survey 2006, 2009.

[1]         See
European Commission (2009), Chapter 1 ‘Root causes of the crisis’ and Chapter 2
‘The crisis from a historical perspective’. See also European Commission
(2010b), ‘Surveillance of Intra-Euro-Area Competitiveness and Imbalances’. On
the difficulties to deal with these imbalances ex ante, see Wolf (2012).

[2]           In 2012 the
European Commission initiated a monitoring program called the Macroeconomic
Imbalances Procedure (European Commission (2012)). See the Alert Mechanism
Report COM(2012) 68 and the in-depth country reviews published as European
Economy - Occasional Papers, DG Economic and Financial Affairs, European
Commission.

[3]         When a the crisis is large
enough to drag down an exposed financial sector, efforts from the government to
prevent a meltdown of the financial system increase the risk that private debt ¾e.g. mortgage backed assets in private banks
balances¾ becomes public via the
bail-out of the troubled banks. This risk is at the origin of the subsequent
sovereign debt crisis. This is what happened in Ireland in 2011 and with Spain in 2012 and it is a classical feature of this type of recessions (see Reinhart and
Rogoff (2011)).

[4]         See
section 1.3 in the European Competitiveness Report 2011.

[5]         There are several reasons why
prices may take long to adjust. First, households tend to hold the property in
the hope that the price will recover in the future and in order to minimize
losses. Second, for analogous reasons, banks tend to refinance loans to
developers in order to delay the realization of losses. Both strategies result
in a low number of properties on sold in the market, and hence a low pressure
on observed prices to go down.

[6]           Details of the
reaction of different components of aggregate demand can be found in Table 1.7
in the appendix. It may be noted that this chart would not look very different
if UK and Sweden would be replaced, for example, by Spain and Germany, so it does not seem that belonging to the euro or not is making any significant difference
as far as the recovery is concerned. The development of internal imbalances
seems to have played a more important role.

[7]         A
more detailed description of recent trends and development can be found in the
European Commission's Labor Market Review (available at: http://ec.europa.eu/economy\_finance/publications/european\_economy/labour\_market\_en.htm).

[8]         For the dual labour market see
chapter 3 in Employment in Europe 2010 (European Commission (2010a)). For a
comparative analysis between France and Spain see Bentolila et al. (2011).

[9]         In bad times households tend
to postpone the purchase of durable goods, typically large and expensive items such
as cars and some electric appliances that do not need replacing in the
short-term. Analogously, liquidity- and/or credit-constrained firms tend to
postpone investment decisions when business conditions are uncertain. This is a
well-documented empirical regularity in normal business cycles but also in
recessions: see Hall (2005, table 2.4) for a summary of the behaviour of
sectors in recessions in the US in 1948-2001.

[10]        As a matter of fact, in most
countries the construction sector grew labour-intensively with productivity
dropping significantly. In that sense Denmark was an exception and productivity
in fact grew. See the discussion in chapter 1 in European Competitiveness
Report 2011.

[11]          The Alert Mechanism
Report COM(2012) 68 monitors internal imbalances looking at changes in deflated
house prices, private sector credit flow, private sector debt, general
government debt and a 3 year average of unemployment rate. This chapter is
primarily concerned with private sector debt and in particular with households'
leverage.

[12]          Table 1.7 in the
appendix to this chapter details the reaction of the different components of
GDP as well as net exports for all EU Member States.

[13]          See Table 1.7 in the
appendix and the Short-term Industrial Outlook, July 2012, DG Enterprise and
Industry, European Commission.

[14]        Incidentally, the belief that
the multiplier is larger than unity constitutes the ground on which fiscal
stimulus are justified. If the government narrows to increase public
expenditure, and income increases more than proportionally, there is room to
boost demand in the short-term and, at the same time, increase revenues enough
to pay back the debt. This is the classical so-called Keynesian approach to
fighting recessions.

[15]          This is an old idea
recently partially formalized in Melitz (2003). Although the paper focuses on
the (static) gains from trade liberalization, it is easy to see how these
competitive pressures will also provide incentives to adopt and develop new
technologies sustaining (dynamic) long-run growth. For an overview of this
literature see Bustos (2010), Lileeva and Trefler (2010) or Constantini and
Melitz (2008) among others.

[16]          See, for example,
Legrain (2008) for a description of the development of the electronics industry
in Taiwan and its connection with Taiwanese migrants in the US.

[17]          See the 12 pillars
of competitiveness mentioned in the Global Competitiveness Report 2012, World
Economic Forum.

[18]        In the case of goods, the
share of EU exports over total exports of all Member States has been quite
stable in the last 20 years. See the discussion in section 1.6 below.

[19]          The larger an
economy, the larger the variety of goods, and hence the less need for trade. In
the limit the planet has zero trade with the rest of the universe, at least so
far; this point was famously made in Krugman (1978).

[20]        Actually the share is stable
not only for Germany but for the EU as a whole as well (See Chapter 2 in
European Competitiveness Report 2010).

[21]          The picture is
slightly different for imports. China has become a major source of German
imports. In this respect, however, Germany is no different from many other
advanced economies, and while Chine has become an
important source of imports (9% total), traditional trade partners still
constitute the bulk of German imports.

[22]         The hypothesis that
the boom period has diverted resources from the "real economy" was
examined in chapter 1 in the European Competitiveness Report 2010. It turned
out that countries that overinvested in dwelligs also increased productive
investment or exports. In other words, beyond the obvious oversizing of the
construction sector, it does not seem that productive sectors were drained
resources. This is likely explained by the net borrowing abroad.

[23]          Comparable data for
EU-12 Member States is not available although it is easy to expect a surge in
exports to the EU since the mid-1990's.

[24]        Of course, this does not mean
that trade was not affected by the crisis. The implication is rather that the
EU was not impacted differently from the average trading country in the world.

[25]          A decreasing share
can be due to poor performance (exports growing more slowly than other
countries) or to a composition effect (volume of trade growing because of new
actors coming in). When all major industrial powers are losing trade shares,
the composition effect is the only reasonable hypothesis: it is developing
countries joining international trade.

[26]        Data
for the nominal comparison, is from the AMECO database, GDP a current market
prices, EU-15=100. For the PPS comparison, Penn World Table.

[27]        The
reader may also refer to the more systematic analysis of export performance in chapters
3 in the monograph devoted to the recovery of trade in the Quarterly Report of
the Euro Area 2012-2.

[28]        See Darvas (2010) for a review
of the European fiscal crisis in comparison to the US with a special reference
to the case of Greece and the revenue-side of the problem. See also Henning and
Kessler (2012) for a more general comparison of the building of the American
and European monetary, fiscal and banking area.

[29]        The
UN Manual on Statistics of International Trade in Services 2010
distinguishes: Business services, Communication services, Construction and
related engineering services, Distribution services, Educational services,
Environmental services, Financial services, Health-related and social services,
Tourism and travel-related services, Recreational, cultural, and sporting
services, Transport services, Other services not included elsewhere.

[30]        The literature on the
export-led growth hypothesis examines whether exports induce changes in the
rate of technical change. That is, the possibility that exports can induce
sustained growth beyond the obvious instantaneous impact on income. If this
literature is inconclusive, this is reflected in this weak relationship observed
in EU recent experience.

[31]        The disclaimer implicit in the
use of the expression "most likely" is due to the possibility that
this chart reflects the reverse causality: e.g. rich countries can afford an
efficient administration.

[32]          On the limited role
of price-competitiveness, see
chapters 1 and 2 in the monograph devoted to the recovery of trade in the
Quarterly Report of the Euro Area 2012-2.

[33]
              Bhagwati
et al. (2004) drew attention to the problem of the lack of a consistent use of
definitions.

[34]
              The
Eurostat survey uses the term ‘international sourcing’. According to Alajääskö
(2009), captive offshoring is about twice as common as offshore outsourcing in
the sample.

[35]               In addition to the economics
literature, papers on these concepts can be found in the business, management
and economic geography literature; understandably, the focus of these is
different.

[36]        See the Annex for a short
description and www.wiod.org for a detailed description of the world
inpout-output database. The WIOD project was funded by the FP7 SSH research
programme.

[37]          See Stehrer et. al.,
(2012) for a more detailed description.

[38]        The
Leontief inverse is used in input-output analysis in order to take into account
that the output of a certain industry i needs the outputs of a number of other
industries n in order to satisfy the demand for a product from industry i.

[39]          See also the
analyses of energy content in Japanese services exports in Chapter 3.

[40]        European
Commission (2010) "Trade as a driver of prosperity". Commission
staff working document accompanying the Commission’s Communication on “Trade,
Growth and World affairs”.

[41]        The WIOD project was funded by
the FP7 SSH research programme.

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