MEMOIRE

P r é s e n t é e n v u e d e l ' o b t e n t i o n d u M a s t e r e n S c i e n c e s é c o n o m i q u e s , f i n a l i t é E n t r e p r i s e s

Determinants of European R&D offshoring: A gravity model of R&D offshoring flows in Europe

Par Sébastien Bouvy Coupery de Saint Georges

Directeur: Carine Peeters Assesseur: Pierre-Guillaume Méon

Année académique 2010- 2011

Determinants of European R&D offshoring: A

gravity model of R&D offshoring flows in Europe

May 23, 2011

Abstract

A new trend in offshoring processes appears and concerns R&D ac-

tivities. This paper tries to shed some light on different factors which

influence the decision to offshore ones innovation centres. To do so, we

focus on intra-European offshoring flows by taking a sample of 15 Euro-

pean countries and take the bilateral transactions in R&D services from

the Balance of Payment as a proxy for this kind of flows. Based on the

gravity equation model, we use three different estimation methods (OLS,

transformed-OLS and PPML) to compare and get the most relevant coef-

ficient estimators of our explanatory variables. Our results show that the

more partners are close in terms of distance, culture and income level the

more they do R&D offshoring. Furthermore, there is a reciprocal knowl-

edge transfer between West and East Europe and so R&D offshoring tends

to spread innovation throughout Europe. In contrast with other studies,

a high proportion of well-educated people in a country does not seem to

be a significant factor in the decision to offshore. Another implication is

that the good quality of institutions favours offshoring of R&D centre in

Western countries. Such results provide some potential explanation why

a European company decides to offshore its innovation centres opening

for further studies about the same topic.

Acknowledgments

I wish to thank Carine Peeters, my thesis director, for her support during my

research for this paper and also Julien Gooris who helped me to modelise as best

as possible my data. I thank my parents for their support during my studying.

I would like to thank particularly my girlfriend who stood by me for this last

year of research and writing.

Contents

Acknowledgments 1

Contents 2

List of Figures 4

List of Tables 4

1 Introduction 5

2 A broad overview on offshoring 8

2.1 Offshoring vs. outsourcing . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Literature review and offshoring trends . . . . . . . . . . . . . . . 9

3 The gravity equation model 13

3.1 The classical gravity equation model . . . . . . . . . . . . . . . . 13

3.2 Empirical background . . . . . . . . . . . . . . . . . . . . . . . . 16

4 Data description and econometric aspect 19

4.1 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Statistical discussion . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.1 Which are the top favourite locations for offshoring? . . . 22

4.2.2 Which are the top importers of R&D offshored services? . 23

4.2.3 The importance of education . . . . . . . . . . . . . . . . 23

4.2.4 Offshoring flows between blocs . . . . . . . . . . . . . . . 24

4.3 Econometric specification . . . . . . . . . . . . . . . . . . . . . . 25

5 Empirical analysis 28

5.1 Determinants of R&D offshoring flows . . . . . . . . . . . . . . . 28

5.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2.1 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2.2 Checking by bloc of countries . . . . . . . . . . . . . . . . 34

2

6 Conclusion 37

References 41

Appendix 46

Appendix A: Silva and Teneyro’s model . . . . . . . . . . . . . . . . . 46

Appendix B: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Appendix C: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3

List of Figures

1 Products and occupations: the firm matrix . . . . . . . . . . . . 48

2 Selected countries . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3 Share of each European bloc in the R&D offshoring inflows on

average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4 Highly educated population and gross domestic expenditure in

R&D on average over the period 2007-2009 . . . . . . . . . . . . 51

5 Relation between the weight in the sample of country’s size and

offshoring inflows . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

List of Tables

1 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2 Classification of countries . . . . . . . . . . . . . . . . . . . . . . 53

3 Kaufmann indicators of governance . . . . . . . . . . . . . . . . . 54

4 Means of R&D offshoring flows in current euros by pair of coun-

tries over the period 2007-2009 . . . . . . . . . . . . . . . . . . . 55

5 Means of R&D offshoring flows in current euros by pair of coun-

tries over the period 2007-2009 (continued) . . . . . . . . . . . . 55

6 Means of highly educated population over the period 2007-2009 . 56

7 Means of gross domestic expenditures in R&D in current euros

over the period 2007-2009 . . . . . . . . . . . . . . . . . . . . . . 57

8 Empirical results for Western European countries bloc . . . . . . 58

9 Empirical results for Southern European countries bloc . . . . . . 59

10 Empirical results for Central and Eastern European countries bloc 60

11 Correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 61

12 Correlation matrix (continued) . . . . . . . . . . . . . . . . . . . 61

4

1 Introduction

Since the 80s, there have been different waves of manufacturing offshoring all

around the world in order to benefit from the lower labour costs in certain coun-

tries. Indeed, one of the key drivers to offshoring is cost savings. However, there

are several other reasons and advantages such as the access to distinctive skills1

and growing performance from fast-developing economies, particularly in Asia.

Currently, companies are meeting a new round in the offshoring trend: they

are likely to think about other ways to improve their structures in R&D2. The

most recent offshoring is linked to innovation. According to Bardhan (2006),

the globalisation process and the intensification of competition have forced en-

terprises to redesign their management structure and take into consideration all

cost sources, including R&D and innovation-related activities.

More precisely, the international trade theory has a limited consideration as

to the effects of offshoring on R&D activities in the country of origin. Certain

authors such as A. Naghavi and G. Ottaviano (2006) tried to fill the gap in the

international trade literature on the dynamic effects of offshoring on R&D. The

authors determined that, when offshoring reduces the feedback from offshored

plants to domestic labs, it is likely to bring dynamic losses when the countries

of origin are large, and in sectors in which R&D is cheap and product differen-

tiation strong. In their endogenous growth model, offshoring of R&D induces

some coordination problems between the offshored and domestic divisions of a

corporation.1Skills are likely to be available in abundance. For instance, China produces 350,000

engineering graduates each year compared to 90,000 in the U.S.A. - “Offshore bonanza: Smart

firms look beyond mere cost savings”, Strategic Direction (2006), Vol. 22 Issue: 5, pp. 13-15.2The following definition of R&D comes from the OECD summary of Frascati Manual

which helps national experts in OECD countries to collect and issue R&D data: “Research

and experimental development (R&D) comprise creative work undertaken on a systematic

basis in order to increase the stock of knowledge, including knowledge of man, culture and

society, and the use of this stock of knowledge to devise new applications. R&D is a term

covering three activities: basic research, applied research, and experimental development.”

5

Another element to consider is the decision to offshore one’s innovative depart-

ment to a foreign location. Indeed, instead of keeping its research centre in a

domestic location a corporation may decide to set up a foreign affiliate which

will focus on innovation or to subcontract such activities to a foreign partner.

This strategy might be either to benefit from specific factors in a particular area

(a lot of highly-skilled people in a foreign location, capital intensive area, etc.)

or to cut costs by paying lower wages for the same level of skills compared to

the national level.

However, companies should take into account the complementarity of home and

offshored R&D activities to achieve a competitive advantage. As D’Agostino et

al. (2010) suggested, the complementarity between domestic and foreign inno-

vative assets depends on their natures and their complexity. In fact, the home

and offshored R&D activities are complementary if they are not similar as well

as when offshored R&D activity is concerned with modular and less complex

technologies. This finding is based on the geographical technological specialisa-

tion and the reverse knowledge transfer from the offshore locations to the home

regions.

Moreover, when looking at the structural attributes of R&D offshoring, there

are common characteristics to the offshoring of services compared to the off-

shoring of manufacturing activities (see Bardhan (2006)). Actually, R&D off-

shoring and manufacturing offshoring are both more capital intensive than ser-

vices offshoring. In terms of effects on jobs in the home country, manufacturing

offshoring influences contiguous and similar skills and occupations within the

blue-collar workforce, whereas outsourcing/offshoring of services and R&D af-

fects white collar jobs across dissimilar occupations. Manufacturing offshoring

can be viewed as impacting along product lines, whilst, services offshoring is

impacting along occupational/job lines. R&D offshoring is a mix of both. The

development of a new product would initially be part of manufacturing off-

shoring but this kind of activity requires specialised occupations/jobs such as

6

scientists, engineers and so forth. This is the reason why services offshoring

affects other occupational lines compared to R&D offshoring (see Figure 2 in

Appendix B).

The purpose of this paper is to provide a new vision on the dynamics of off-

shoring innovation-related activities. This vision is based on the idea that there

are offshoring flows similar to trade flows between countries and that these move-

ments can be determined by different factors. In this paper, the gravity equation

model is used to set the relation between R&D offshoring flows in Europe and

the most relevant variables. The gravity model was used in several manners

to assess different flows. However, originally, this model was constructed to

analyse international trade and was applied by some pioneers like Tinbergen

(1962), Pöyhöhen (1963), and Linneman (1966). The theoretical basis of the

model came later after many other applications such as in the FDI flows between

countries (Brainard (1997); Mello Sampayo (2009)). The theory which underlies

the gravity model explains that the shorter the distance between two countries,

the greater the intensity of trade activity between those countries. Moreover,

the international trade flows increase with country size and decrease with trade

costs i.e. transportation costs which is represented by distance between nations.

Using the gravity equation model specifications, this study targets to find what

are the relevant determinants of R&D offshoring flows within European coun-

tries. This paper is divided in five other sections where Section 2 clarifies the

difference between offshoring and outsourcing concepts and provides a review of

the literature about R&D offshoring topic. Section 3 explains the basics and the

evolution of the gravity equation model and then provides an empirical back-

ground of this model linked to our subject. Section 4 describes the data, the

sample used, and the econometric aspect of this study. Afterwards, Section 5

brings the results of the estimation. Finally, the paper ends with a conclusion.

7

2 A broad overview on offshoring

2.1 Offshoring vs. outsourcing

Many people do not know the clear difference between offshoring and outsourc-

ing and, very often, use both terms without any deep comprehension of what

they are. The following study is based on the definition in Bardhan’s (2006)

paper: foreign outsourcing is arms length sourcing to suppliers abroad, and

intra-company offshoring is the transfer of production abroad to foreign affili-

ates and subsidiaries of European companies, with the objective of exporting the

output back to the Europe. This definition clarifies the concepts of outsourcing

and offshoring in terms of investment decisions.

A domestic company may decide to invest to create a foreign affiliate so that

the latter conducts a certain activity instead of its parent (e.g. manufacturing

activity, IT services, etc.). This action is called by Lewin et al. (2008) captive

offshoring i.e. the domestic firm keeps the control by owning the majority of the

shares of its foreign subsidiary. On the other hand, the national enterprise can

decide to offshore certain activities by subcontracting with a foreign partner.

This is the offshore outsourcing decision where the foreign partner has the total

control of its supply to the domestic company. The offshoring decision has two

main implications for the concerned company either it decides to offshore and to

outsource its IT services, for instance, or it invests abroad into a subsidiary in

order to offshore and insource its IT services. Hence, we consider two categories

of offshoring: captive offshoring and offshore outsourcing. The key difference

between these two concepts is based on the control from the home company on

its offshored activities.

8

2.2 Literature review and offshoring trends

The typical view on offshoring is always defined by the Northern countries which

offshore some of their basic activities to the Southern countries in order to ex-

ploit a cost advantage in those locations. Antras and Helpman (2004) defined

a theoretical model with two countries, the North and the South, for analysing

the global sourcing strategies. They found that “high-productivity firms acquire

intermediate inputs in the South whereas low-productivity firms acquire them

in the North. Among firms that source their inputs in the same country, the

low-productivity firms outsource whereas the high-productivity firms insource.

In sectors with a very low intensity of headquarter services, no firm integrates;

low-productivity firms outsource at home whereas high-productivity firms out-

source abroad.” Outsourcing can also happen between vertically integrated

firms. Helpman (1984) introduces a model of vertical foreign direct investment

in order to explain the intra-firm trade related to the intra-firm international

outsourcing.

According to PRTM, a large management consultancy firm, and World Trade

magazine survey the first concern for a large number of companies in the US,

Europe and Asia is the offshore transfers and related outsourcing topics. The

survey found also that this issue is not the inherent prerogative of the huge

MNEs3 as many small and medium-size structures are intensively prospecting

offshore opportunities. Moreover, we know that since the 1980s outsourcing

of manufacturing activities to low-cost countries is usually practised (Dunning

(1993); Lee (1986); Vernon (1966)) and even more routinised now. The survey

shows that offshoring decisions are not limited to manufacturing industry but

also apply to a wide range of industries, “[...] from consumer services to high

tech.”

3The acronym MNE refers to “Multinational Enterprise”.

9

Looking at the material outsourcing, a large part of the studies found an in-

creasing extent of international outsourcing of material inputs over time (see

Feenstra and Hanson (1996), Campa and Goldberg (1997), Hummels, Ishii and

Yi (2001), Yeats (2001), Hanson, Mataloni and Slaughter (2004), and Borga and

Zeile (2004)). Additionally, Egger and Egger (2006) show that, for European

countries, there is a negative impact of international material outsourcing on

the productivity of low-skilled workers in the short run, whereas there is a pos-

itive impact in the long run. Empirical evidence in the United States (Feenstra

and Hanson, (1996, 1999)) and the United Kingdom (Hijzen et al., (2002)) show

also that outsourcing of unskilled labour-intensive parts of production processes

from relatively skilled-abundant countries to unskilled-abundant countries leads

to an increase in the relative demand for skilled labour in the skilled-abundant

country and hence increases the skills premium.

For at least a decade, there has been a new trend of globalisation which is

concerned by the internationalisation of services trade and became really im-

portant in the total value of trade around the world. As Dossani and Panagariya

(2005) explained, some developing countries such as in Asia have become large

suppliers of services for developed nations. The increase of this type of trade is

due to more offshoring for this kind of activities and concerns a large range of

services like “back-office services such as payroll; customer-facing services such as

call-centres and telemedicine; design services such as the design of application-

specific integrated circuits; research services such as conducting clinical trials;

software services such as programming; and IT and infrastructure outsourc-

ing such as the managing of corporate e-mail systems and telecommunications

networks.” The same authors argued that the largest growth in offshoring is

happening in business services4.4“Business processes is a general term to refer to the collection white-collar processes that

any bureaucratic structure undertakes in servicing its employees, vendors, and customers such

as human resources, accounting, auditing, customer care, telemarketing, tax preparation, etc.”

- Rafiq Dossani and Arvind Panagariya (2005).

10

With respect to the job reallocation issue, R&D offshoring can lead to some im-

portant consequences in the workplace of both developed and developing coun-

tries. Knell and Rojec (2009) studied the job reallocation issue at the European

level by using the dataset of the publically available European Restructuring

Monitor (ERM). They found that at least half of all European offshoring oc-

curs within Europe. Then, India is larger than China as a source of offshoring,

mainly because of the huge volume of offshoring in the service industries (e.g.

call centres). In order to lower labour costs and have access to well-educated

pool of workers, European offshoring is moving principally to Eastern Europe.

These authors pointed out that offshoring induces the movement of low-skill

jobs out of Western Europe whereas offshoring of innovation-related activities

and the relatively high-skill jobs remain within Western Europe.

The press links offshoring with job losses but Amiti and Wei (2004) show that

there is no evidence to support this assumption. In fact, a large part of developed

nations are not specifically more outsourcing-intensive than many developing

countries. More precisely, many developed regions tend to have surplus i.e., the

rest of the world outsources to them rather than the contrary. The top providers

in services are firstly, the United States and secondly, the United Kingdom. The

authors explained that service outsourcing would not induce a reduction in ag-

gregate employment while it has the potential “to make firms/sectors sufficiently

more efficient, leading to enough job creation in the same sectors to offset the

lost jobs due to outsourcing.”

According to Amiti and Wei (2004), despite the early offshoring of manufactur-

ing activities, the offshoring of high-value adding activities remains a relatively

undiffused practice. In fact, innovation-related activities are still difficult to off-

shore because they imply intangible goods such as the knowledge, the skills, the

education, etc. Furthermore, the domestic firm may have to support a higher

risk in this kind of offshored activities as its product development depends on

the ability and the availability of highly-skilled people in a too distant foreign

11

location to provide the expected results. Consequently, the distance between

two entities depending on each other is important because one needs to sell

new products or new services resulting of an intensive R&D activity to grow

profits and another needs the previous one because its production has not any

value outside their relationship. The information asymmetry can become a huge

problem in the relationship between offshored activity and the domestic parent

or partner as well.

On the other hand, the new wave of offshoring of R&D activities originates

from a change in the business model of firms. As Bardhan and Jaffee (2005)

explain, the individual is experiencing a transformation from a model of pro-

prietary, internal, intra-firm or domestically-based industrial laboratory to an

offshoring model. This change is due to at least one major reason which is the

increasingly global nature of sales of large firms. Indeed, if a firm expands its

market share throughout the world it needs to design its products in line with

local tastes, leading to the strategy to “design to the market” and even to “design

and research to the market” which adds to the previous strategy to “produce

to the market”. According to Bardhan and Jaffee, there is a huge potential of

skilled labour in China and India. In consequence, there is an outward transfer

of R&D activity to India, for instance, in software, bio-technologies, pharma-

ceuticals, engineering design, and development areas.

A large pool of highly skilled workers in emerging countries constitutes a pre-

requisite to offshore innovation-related activities. This is a new key strategic

driver (Bunyaratavej et al., 2007; Deloitte, 2004; Farrell et al., 2006; Lewin &

Couto, 2007; Lewin & Peeters, 2006) and implies more than just the offshoring

of IT activities or business processes. As explained by Manning et al. (2008),

offshoring involves now product development and product design and these phe-

nomena might influence what the authors call the global sourcing of Science and

Engineering (S&E) talent. Based on the annual Offshoring Research Network

survey results, a large part of US and European companies have started to em-

12

ploy S&E skills in different areas in the world. This trend is due to a shift

of clusters providing highly-skilled people from Western countries to emerging

nations such as China and India which have invested more in education and

innovation in order to curb and gradually “reverse” the brain drain.

3 The gravity equation model

For this paper we decided to use the gravity equation framework to assess the

R&D offshoring flows because of the wide empirical history and applications

of this model on bilateral trade flows in the beginning (see Tinbergen (1962)),

and on other flows like FDI between nations later on. This model generally

provides interesting macro-level results about the influences of some factor on

trade-flows. In our case, this is a completely new application of the gravity

equation model which estimates the relationship between offshoring flows and

some determinants. The next sub-section provides the basics about the theoret-

ical aspects of the gravity model and the other sub-section presents an overview

of the empirical literature.

3.1 The classical gravity equation model

In the standard gravity equation, trade flows between a pair of countries are

proportional to their masses (GDPs) and inversely proportional to the distance

between them. Numerous studies used the basic form of this model and showed

relevant empirical results. This form is expressed as following:

Mij = αY βi Yγj N

δi N

εj dµijUij

5 (1)

where Mij is the trade flow of goods or services from country i to country j,

Yi and Yj are GDPs of i and j, Ni and Nj are population of i and j, and dij

is the distance between nations i and j. Usually, we assume that the Uij term

is a lognormal distribution error factor with E(ln(Uij)) = 0. Some authors like5This equation comes from Anderson’s paper (1979) where he explained the theoretical

foundations of the gravity equation model.

13

Anderson (1979) defined the theoretical foundations of this model which had

firstly more empirical specifications.

On the other hand, according to Kimura and Lee (2006), it has been found

that the gravity model can be deducted from different models as Ricardian,

Hecksher-Ohlin and the monopolistic competition model. Indeed, Helpman and

Krugman (1985) have shown the possibility to derive the gravity equation from

the monopolistic competition model with increasing returns to scale. Moreover

Deardorff (1998) found that one can derive a gravity model from a Heckscher-

Ohlin model without assuming product differentiation. A gravity relationship

has been put in evidence by developing a Ricardian model of trade in homoge-

neous goods (see Eaton and Kortum (2002)). As a result, the gravity equation

is part of any model of international trade.

The gravity equation model was used by Frankel and Romer (1999) to assess the

influence of trade on growth by using the same bilateral trade data as Frankel

et al. (1995) and Frankel (1997). This database combines a sample of 63 coun-

tries for the year 1983. The authors drop from their database the observations

where registered bilateral trade is zero. Their findings fit other empirical results

i.e. trade as a fraction of GDP is negatively correlated to distance, is positively

correlated to the size and population of the jth country, etc.

Despite its successful applications and theoretical basis, the gravity equation

from an empirical point of view has some limitations and mismatches when

there is no trade between a pair of countries. Indeed, the majority of empirical

studies log-transformed the bilateral variables (trade, FDI, etc.) in order to have

a consistent log-normal distribution depending on the log-normal distribution

of explaining variables. However, this log-transformation eliminates a part of

the observations on the bilateral dependent variable i.e. for the zero-value. As

a result, the researchers lost some part of the information which may be rele-

vant. To overcome this problem some authors found simple solutions such as

14

adding one to all observations of the dependent variable in order to get, in log-

term, zero-values6. A drastic solution is to drop the pair of countries with zero

trade from the data set and afterwards estimate the remained log-transformed

observations by OLS. Unfortunately, those methods can produce inconsistent

estimators of the parameters of interest.

Another problem with the log-linearisation, quoted by Silva and Teneyro (2006),

is the heteroskedasticity. This leads to have inconsistent estimators and “if er-

rors are heteroskedastic, the transformed errors will be generally correlated with

the covariates.” These authors propose a solution based on a constant elasticity

model to the different problems linked to the log-transformation (for details,

see Appendix A). By conducting a simulation study, they found that a Pseudo-

Poisson-Maximum Likelihood method is the most efficient resolution in com-

parison to other estimation methods (Tobit, NLS and OLS). Indeed, according

to their results, the “income elasticities in the traditional gravity equation are

systematically smaller than those obtained with log-linearized OLS regressions.

In addition, in both the traditional and Anderson−van Wincoop specifications

of the gravity equation, OLS estimation exaggerates the role of geographical

proximity and colonial ties.” Consequently, the regression analysis of this paper

is built on the comparison of different estimation models as PPML in order to

get the best and the most relevant estimators.6Some raw data for the bilateral dependent variable Tij can be equal to zero, so the solution

to take into account in the estimation for such observations is explained as follows:

Adding one to the raw data of Tij variable:

1 + Tij (so, the zero-values take the value one)

In log-term:

log(1+Tij) (then, the observations equal to one (i.e. zero-values, in raw data term) are equal

to zero, in log-term).

15

3.2 Empirical background

The present study does not focus on the corporate-level decision to offshore

its key activities but tends to estimate the importance of some factors on the

R&D offshoring flows in Europe. In doing so, one has to bear in mind the pre-

vious explanation about the two key concepts of this paper (see Section 2.1).

In addition, we conduct a study on an aggregated level i.e. on the bilateral

country flows. Other studies focused on a more disaggregated level about the

relationship between trade and innovation-related activities. Uzagalieva et al.

(2010) used this approach to assess the relationship between innovation expen-

ditures and the intra-industry trade flows in European markets. These authors

concentrated on the imitation and innovation concepts which are important

modes of technological development. They used a gravity equation model to

estimate the potential progress effects of innovation and imitation on a sample

of 20 countries. The results are that the increase in size of the science-based

manufacturing industries leads to a greater intra-industrial trade between coun-

tries which approximates innovation-based technological growth. As usual in

the gravity model, the distance decreases the trade flows. R&D expenditures

have a significant and positive influence on the progress indicator.

Regarding the effect of technological innovation on international trade, Ramos

and Martinez-Zarzoso (2009) find that it has a positive impact on export perfor-

mance but also that it is a non-linear relation. There is a U-shaped relationship

between exportations and creation of technology and between exportations and

diffusion of old technology. However, the relations between exports and diffu-

sion of recent innovations and between exports and human skills are defined

by an inverted U-shaped chart. To overcome the complexity to capture all the

aspects of technologies, they used in their empirical analysis an index called

Technological Achievement Index7 which is based on four dimensions: creation7This composite index was firstly introduced in 2001 by UNDP in its Human Development

Report 2001 - Source: UNDP (2001) Human Development Report 2001, Oxford University

Press, New York.

16

of technology, diffusion of recent innovations, diffusion of old innovations and

human skills.

When assessing whether better information can eliminate the effect of geograph-

ical distance, Loungani et al. (2002) find some heterogeneity between developed

and developing nations. Indeed, within the different determinants of interna-

tional trade technological innovation constitutes “a substitute for distance in

developing countries (better information lowers the effect of distance), whereas

technological innovation and distance are complementary in developed countries

(better information magnifies the effect of distance)”. Furthermore, Fink et al.

(2005) show that communication costs on bilateral trade flows have a significant

effect and they have a greater weight when exchanging differentiated products

compared to exchanging homogeneous products. These empirical results show

that it is important to take into account and to bear in mind the non-linear

influence of technological progress on trade flows.

Dollar and Kray’s (2003) paper show that the quality of institutions consti-

tutes a great determinant of trade flows in our economy. For instance, the rule

of law factor measures the level of corruption in a country and has a clear im-

pact on the level of trade in the concerned country. An exporter have to support

risks linked to the business and corruption might increase it more than other

factors. According to De Groot et al. (2004), the institutional quality has a

clear and positive effect on bilateral trade flows. They used a gravity equation

model to estimate the influence of institutions on trade. Their model shows

that good governance lowers transaction costs for trade between high-income

countries, while trade between low-income countries suffer from insecurity and

transaction costs.

The regional trade agreements (RTAs) have an influence on trade flows between

countries. Some authors studied this kind of determinants within European

countries. According to Stack (2009), the RTAs effects on trade focus on the

17

enlargement process rather than the deepening of trade integration between EU

members. She quotes that in a part of the empirical literature the sign and

significance of trade policy effects can differ. This is due to the existence of bias

because of omitted relevant variables in the analysis. Stack used a dataset of

bilateral flows from 12 EU countries to 20 OECD trading partners between 1992

and 2003. The results show that the positive and significant coefficient estimate

of the European trading bloc dummy variable declines in magnitude with an

increasing degree of heterogeneity in the model. According to these results, it

is difficult to quantify the effect of European integration on trade flows.

Martinez-Zarzoso and Nowak Lehman (2003) studied another free trade agree-

ment between the Mercosur and the European Union by using a gravity equation

model. They used a panel of data from a sample of 20 countries (Mercosur with

Chile and the EU15 bloc of European countries) in order to clarify the time

constant country-specific effects and also to take into account of relationships

between the relevant variables over time. They found that the fixed effect model

is more relevant compared to the random effect gravity model. They added some

variables to the basic gravity equation and the estimation results show that the

infrastructure, income differences and exchange rates are important explana-

tory variables for bilateral trade flows. Specifically, the exporter and importer

incomes have a positive influence on trade between these two blocs of nations.

The tax policy in a specific region can be also an interesting determinants of

trade flows within Europe. Hansson and Olofsdotter (2008) studied the influ-

ence of tax differential on a sample of bilateral FDI flows for the European

Union members over the period 1986-2004. They found that tax differentials

are important determinants explaining FDI flows. Indeed, the marginal effective

corporate tax rates between host and investing countries have a negative im-

pact on FDI flows. De Mooij and Ederveen (2006) argued that tax differentials

influence FDI, but that the magnitude vary substantially and is sensitive to em-

pirical specification as well as time periods and countries considered. Because

18

of those shortcomings, the present paper does not include the taxation issue in

its estimation of R&D offshoring flows in Europe.

4 Data description and econometric aspect

4.1 Data sources

Our bilateral dependent variable data over three periods (2007, 2008 and 2009)

comes from the Eurostat database. This dependent variable is part of the

Balance of Payments (BoP)8 of our sample composed by 15 European coun-

tries (Austria, Bulgaria, Cyprus, Czech Republic, Denmark, Germany, Greece,

France, Italy, Latvia, Lithuania, The Netherlands, Poland, Romania, and Slo-

vakia). In fact, this is the bilateral transactions in R&D services between resi-

dents and non-residents of a given country i.e. the outward flows (recorded as

total value of credits in the BoP) in R&D services from country i to country j.

This paper focuses on the R&D offshoring flows throughout Europe and, there-

fore, we consider this variable as a proxy of offshoring flows within European

partners. We assume that the transaction flows between a pair of countries

in R&D services is the sum of payments exporting firms, located in country i,

receive from foreign external partners or foreign affiliates/parents, situated in

country j, in delivering offshored (in- or outsourced) R&D services as a result

of the offshoring of innovating-related activities in country i. Our assumption

and proxy variable for R&D offshoring are in line with the statement from Van

Welsum and Reif’s (2005) paper that there does not exist direct official data

measuring the extent of offshoring. These authors take trade in total services8“The Balance of Payments (BoP) systematically summarizes all economic transactions

between the residents and the non-residents of a country or of an economic area during a

given period. The Balance of Payments provides harmonized information on international

transactions which are part of the current account (goods, services, income, current transfers),

but also on transactions which fall in the capital and the financial account.” - Eurostat,

Balance of Payments statistics and International investment positions - Metadata.

19

as a proxy for measuring total services offshoring.

However, in our case it is important to bear in mind that not all trade in R&D

services is linked to offshoring and unfortunately it is not possible to distin-

guish the share of trade in R&D services that is directly related to offshoring.

The sample of countries considered includes 15 countries where each has by

turn the host position and the home position for three periods of time. It is

assumed that the ith country, called “host”, welcomes offshored innovation ac-

tivities and receives payments from the jth country, called “home”, which pays

for offshored R&D services coming from the host country. We take this specific

sample through an inductive process i.e. we selected each country with respect

to the availability of the data for our dependent variable over the considered

time period.

Concerning the databases used to build the independent variables for the regres-

sion analysis, the Eurostat database over three particular years - 2007, 2008,

and 2009 - is taken into account for the share of highly educated people in the

total population (aged 15 to 64 years) i.e. the people who attain at least the first

stage of tertiary education9 (higher education, university degree, etc.) and for

GDP. The reason to consider the first variable is that highly educated popula-

tion constitutes an important factor in offshoring literature (see Bunyaratavej et

al., 2007; Deloitte, 2004; Farrell et al.,2006; Lewin and Couto, 2007; Lewin and

Peeters, 2006) and much more when talking about offshoring complex activities

(see Section 2.2). In order to capture the size-effect on our dependent variable,

we take the GDP of each country of our sample. The level of innovation in

each partner is proxied by the share in GDP of gross domestic expenditure in

R&D whereas the infrastructure level is based on the level of Internet access in

percentage. Both variables may affect the R&D offshoring flows as a country in-9According to the ISCED - the International Standard Classification of Education - UN-

ESCO 1997, the data on highly educated people has a range from the 5th to the 6th level of

education i.e. from the first to the second-level of tertiary education.

20

vesting in innovation and infrastructure is likely to be a favourite location where

companies offshore. The data for the latter variables are taken from Eurostat

as well.

From the GDP data and population data, we calculate the income per capita

disparity variable which is our explanatory variable that capture the effect of

income differences between partners on offshoring flows. This variable is defined

by the difference between GDP per capita of each partner in absolute value. In-

deed, there is an income disparity even in Europe where typically the West is

richer than the East. This gap is likely to have an impact on the choice of each

partner to offshore or not.

The six Kaufmann indicators measuring the quality of institutions10 are part of

our explanatory variables as well (see Appendix C, Table 3 to have a complete

description of each indicator). In line with Dollar and Kray’s paper, governance

may influence R&D offshoring as a country prefers to offshore to a stable econ-

omy with good institutions. Each indicator is linked to a different dimension

of governance. It spreads out from −2.5 to +2.5, the higher the indicator, the

better is the governance. As in Méon and Sekkat (2006), to linearise these indi-

cators and to estimate the elasticities in the regression equation, we added 3.5

to them in order to be able to calculate logarithms.

We built a dummy to capture the membership of both countries taken into

consideration in the Euro Area bloc. The results of Stack’s paper (2009) lead

to add an EMU bloc dummy variable in order to examine the effects of Eu-

ropean integration on R&D offshoring flows. Such a dummy is more relevant

than an EU dummy because our complete sample of countries is part of the10“The governance indicators aggregate the views on the quality of governance provided by a

large number of enterprise, citizen and expert survey respondents in industrial and developing

countries. These data are gathered from a number of survey institutes, think tanks, non-

governmental organizations, and international organizations.” - Kaufmann et al. (2010).

21

European Union whereas the European currency Union dummy evolved over

the chosen time line. Then, the rest of variables were taken from the CEPII

database which provides the distances between capitals and dummy variables

indicating whether two countries are contiguous and share a common official

language. The distance proxies the transportation cost and constitutes a signif-

icant determinant of trade flows. The contiguity and a common language are

respectively geographical and cultural aspects whose effects were studied in a

broad part of the gravity literature and intuitively may have a positive impact

on R&D offshoring. For instance, a company will prefer to offshore a part of its

activities in a close-by location and/or a country with a similar culture in order

to ease communications and keep control on it.

4.2 Statistical discussion

4.2.1 Which are the top favourite locations for offshoring?

If we look at the last column in Table 5 (see Appendix C), the top 5 providers

of services in innovation-related activities are, by descending order: Germany,

Austria, France, The Netherlands, and Italy which have a share in total flows of,

respectively, 30.87 %, 24.67 %, 14.26%, 12.91%, and 9.11%. Therefore, it seems

that lots of firms offshored their innovation centres in those locations in order to

benefit from the highly-skilled labour force and the knowledge from this main

Western European countries. Indeed, these nations compose a large part of the

total highly educated people (see Table 6 in Appendix C) in our sample covering

2007 to 2009. Once exception is Austria which is one of the favourite offshoring

locations but which has not a large highly-skilled labor pool when looking at

its share in the total highly-educated population in our sample (2%). In this

country, the labour factor might be fully exploited in R&D activities and better

than in other countries. For instance, although Poland has a 10% proportion

in the researchers and engineers population of our sample it possesses a small

participation in the R&D offshoring flows. Hence, despite the fact that high-skill

jobs remain currently in Western Europe (see Knell and Rojec (2009)), there is

22

an opportunity for this country to become more and more a favourite offshoring

place thanks to the presence of highly-skilled workers.

4.2.2 Which are the top importers of R&D offshored services?

At the bottom of Table 5 (see Appendix C), we can see that the set of countries

which composed the most favourite locations for offshoring are also more or less

the top importers of innovation services. More precisely, the 5 most important

importers are, by descending order: Germany, France, The Netherlands, Italy

and the Czech Republic which have a share in total flows of, respectively, 40.82

%, 21.31 %, 15.08%, 7.71%, and 4.31%. The difference with the previous set is

that Austria is not present. Austria benefits from R&D centres set up within

its borders by foreign companies and becomes one of the largest net providers of

R&D services. Germany, France, The Netherlands and Italy are on both sides:

on one hand, providers of R&D services and, on the other hand, importers of

R&D services. These nations are most likely to trade together. Hence, there

may be huge intra-offshoring flows within this region as a Western European

company tends to offshore more in other Western European countries than in

other parts of Europe.

4.2.3 The importance of education

A highly-educated population refers to people who attain at least the first stage

of tertiary education (higher education, university degree, etc.) into the age

bracket from 15 to 64 years. Such a population is required in each country

to expand the research in key subjects like biomedicine, biofuels, new business

processes, etc. Consequently, we can assume that the evolution of a highly-skills

population is positively correlated with gross domestic expenditure in R&D.

Indeed, the levels of this type of expenditures as well as the level of innovation are

dependent from the number of researchers and engineers in a given country. This

is the reason why some foreign companies from different countries where there

are not enough well-educated people might offshore their innovation activities

23

to a location with a large pool of engineers or people with a PhD diploma, for

example. If we compare Table 5, 6, and 7 (see Appendix C), the top performers

in terms of R&D offshoring flows have a huge share in the sample in terms of

gross domestic expenditures. For instance, Poland has an important potential

to become one of the favourite destinations to offshore innovation activities from

foreign companies. This country gathers several advantages like a well-qualified

population which is correlated with larger gross domestic expenditure in R&D

than other Eastern European countries. Figure 4 (see Appendix B) shows a

clear relation between gross domestic expenditure in R&D and highly educated

population.

4.2.4 Offshoring flows between blocs

At a more aggregated level, if we consider some blocs of countries such as

Western European countries (Austria, Germany, Denmark, France and The

Netherlands), Southern European countries (Cyprus, Italy and Greece), and

Central and Eastern European countries (Bulgaria, the Czech Republic, Lithua-

nia, Latvia, Poland, Romania and Slovakia) called respectively WEC, SEC, and

CEEC, we would have other interesting results in terms of offshoring flows. The

weight of Western Europe is clearly dominant in our sample by observing Fig-

ure 3 (see Appendix B). This bloc of countries is composed of 4 out of 5 top

providers of services in innovation activities. Southern, Eastern, and Central

Europe seem to be marginalised and have small weights in the total flows. Fo-

cusing only on the Western bloc, we can observe another key element: almost

half of the volume of services provided by the Western countries is done by

Germany. So, Germany is one of the most favourite places to offshore R&D

activities.

Such differences in offshoring flows between these blocs might be explained by

a simple hypothesis that comes from the theoretical foundation of international

trade. More precisely, this assumption, the main one in the gravity equation

24

model, states that the bigger a country the more it trades with other nations.

Previously, we observed that 5 countries which are the biggest in Europe in

terms of GDP (see Figure 5 in Appendix B) provide lots of services in R&D

which means that many companies from other parts of Europe offshore their

innovation activities in these locations. The weight of these top 5 countries in

the offshoring flows and the share of each of them in GDP terms in our sample

are positively correlated. Looking at Figure 5 (see Appendix B), we observe

that Germany has the highest weight in size and offshoring flows. Table 2 (see

Appendix C) summarises the results according to the dimensions of size and

offshoring inflows performance. This table classifies the different countries of

our sample and, as we can see, Austria has an interesting position as a small

country but with a high performance in R&D offshoring flows. So despite its

smaller size than Germany, Austria has nearly the same weight in the sample in

terms of offshoring flows. The other top countries have an intermediate position

in R&D performance and have a different ranks depending on their size. The

rest of our sample is situated in the bottom-left position on the chart (see Figure

5 in Appendix B). However, we can notice that Poland tends to leave this latter

group.

4.3 Econometric specification

To assess the different determinants of the R&D offshoring flows across Europe,

a gravity equation is specified and estimated. The following equation defines

the additive form of the relation between the offshoring flows and these deter-

minants:

log(Offijt) = β0 + β1.DISTij + β2.EMUijt + β3.CONTIGij

+β4.COMLANGOFFij + β5.log(Eit/GDPit)

+β6.log(Ejt/GDPjt) + β7.log(HRSTit/POPit)

+β8.log(HRSTjt/POPjt) + β9.log(WEBit)

+β10.log(WEBjt) + β11.log(DISPijt) + β12.log(GDPit)

25

+β13.log(GDPjt) + β14.log(GOVit)

+β15.log(GOVjt) + εijt (2)

where log refers to the natural logarithms. Offijt denotes the outward flows

in R&D offshored services from country i, the host country where the inno-

vation activities are located, to country j, the home country in time t. It is

likely that, the more a country i exports R&D services to country j, the more

firms in country j have offshored their innovative activities in country i. The

DISTij variable is the distance between the capital cities of partner countries.

EMUijt is a dummy which denotes if both partners are part of the Economic

and Monetary Union depending on the time period11. CONTIGij variable is a

dummy as both countries i and j are contiguous whereas COMLANGOFFij

is a dummy indicating that both partners share a common official language.

To estimate the effect of some aspects of innovation on R&D offshoring flux,

we take some indicators to proxy the level of innovation in each country, the

level of infrastructure in Europe and the share of Human Resources in Science

and Technology (HRST) in the total population of each country. The level

of innovation in each country is proxied by Eit/GDPit and Ejt/GDPjt which

are the share of gross domestic expenditure in GDP of each partner. WEBit

and WEBjt are based on Internet penetration data (percentage of household

with Internet access) and approximates the infrastructure level in each partner.

The proportion of HRST in the total population is evaluated by the variables

HRSTit/POPit and HRSTjt/POPjt that refer, respectively, to the percentage

of the population of country i and country j - in the age bracket of 15 to 64 years

- which attains the first stage of tertiary education (higher education, university

degree, etc.).

11Some countries of our sample became members of the Eurozone only in 2009 that is the

reason why we have to take into account time for this dummy.

26

The income disparity per capita between country i and j at time t is measured

by the variable DISPijt. GDPit and GDPjt are the gross domestic product of

country i and j in current euros and denote the size of each partner. The last

variables, GOVjt and GOVjt, are based on the six Kaufmann indicators assess-

ing the quality of institutions in our sample of countries. The information from

these indexes was summarised in one variable for each partner via a principal

component analysis (PCA). Indeed, in order to eliminate the high correlation

between the six factors of governance, we transformed these variables in new

variables independently distributed and called principal components. The first

component for, respectively, countries i and j explains mostly the variance of

the dataset (almost 90%) of the initial variables and so we built one variable for

each partner based on it. Finally, the last term of the equation is the error-term

which is assumed to be independently and identically distributed.

Equation (2) is estimated using the Ordinary Least Squares (OLS) method.

In addition to the classical OSL estimator results, equation (2) is transformed

by adding one to all of the observations of the dependent variable. Such a mod-

ification is required to account for the zero-flows in the dataset. In fact, the log-

linearised equation (2) loses a part of information i.e. the zero-flow observations.

By the way, we can compare the estimation results and observe the significance

level of each estimators for both models. However, following the observation

of Santos-Silva and Tenreyro (2006), the Poisson Pseudo-Maximum Likelihood

(PPML) estimation method is used because it seems to be the most appropriate

method to evaluate the gravity equation. Indeed, the log-linearisation provides

bad results when observations with heteroscedasticity are present. As well as

the transformed model, PPML estimation takes into account the zero values

in the dependent variable. Santos-Silva and Tenreyro state also that the OLS

estimation of the gravity equation model magnifies the role of “geographical

proximity and links”. Because of these problems, the authors advise to use the

PPML estimation method (for further explications, see Appendix A). The next

27

equation is estimated through this method:

Offijt = exp[β0 + β1.DISTij + β2.EMUijt + β3.CONTIGij

+β4.COMLANGOFFij + β5.log(Eit/GDPit)

+β6.log(Ejt/GDPjt) + β7.log(HRSTit/POPit)

+β8.log(HRSTjt/POPjt) + β9.log(WEBit)

+β10.log(WEBjt) + β11.log(DISPijt) + β12.log(GDPit)

+β13.log(GDPjt) + β14.log(GOVit)

+β15.log(GOVjt)].ηijt (3)

where ηijt = 1+εijt/exp(xiβ) and E[ηijt|xi] = 1; xi is the matrix of explanatory

variables. The inference method is based on the Eicker-White robust covariance

matrix estimator (see Appendix A).

5 Empirical analysis

5.1 Determinants of R&D offshoring flows

The results of the different estimation methods in Table 1 (see page 30) show

that OLS and PPML have a higher explanatory power than the transformed

OLS in column 2. The R-squared of the latter is only 59% whereas the clas-

sical OLS and the PPML estimations have an R-squared of, respectively, 70%

and 87%. Despite the high explanatory power of the OLS regression type,

the PPML estimation method performs better than the others (see Silva and

Teneyro (2006)).

Looking at the different variables, we can see that the classical measure of dis-

tance coefficient seems significant. The expected negative sign is present in the

three column. If we focus on the third column of the table, the distance which

proxies the cost of transportion constitutes one of the determinants of R&D off-

shoring flows in Europe. As Amiti and Wei (2005) said, the innovation-related

28

activities tends to be difficult to offshore because they imply an important risk

for domestic firms and also intangible apsects such as knowledge, skills, educa-

tion, etc. At the company level, a firm will prefer to offshore to a location close

to its headquarters to keep control and maintain a good communication with

its subsidiaries.

Our results confirms the fact that the distance between two entities depend-

ing on each other is important because one needs to sell new products or new

services resulting of an intensive R&D activity in order to increase profits. Also,

one needs the other one because its production has not value outside their re-

lationship. From an other point of view, at a 1% level of significancy for both

normal OLS model and PPML model, the EMU dummy coefficient is a rele-

vant factor which explains our variable of interest. Indeed, the fact that two

partners are both in the Euro area is positively correlated with the dependent

variable. Two European states which share the same currency will make more

transactions in R&D offshoring terms. It implies that it is necessary for Europe

to go forward in the currency union process in order to create a larger and more

homogeneous market and by the way ease transactions between European com-

panies.

All of the estimation methods are sharing the same view, with the same level

of significancy, on the cultural aspect of each country. Indeed, in Europe, there

are many different cultures, religions and languages in a smaller area compared

to USA where people speak the same language, for instance. In our case, two

European countries with a common official language is positively correlated to

the R&D offshoring flux between them. Hence, being close to each other in

terms of distance and culture are deterministic factors which tend to influence

which region a company will choose either to install an offshored subsidiary or to

contract with an external foreign partner to do R&D activities. This is in line

with the fact that we observe intra-flows among Western European countries

which share a common history, a connected culture and are close to each other.

29

Table 1: Empirical resultsOLS OLS PPML

Dependent variable log(Offijt) log(1+Offijt) Offijt

Distance -0.53*** -2.19*** -0.68***

(-3.94) (-5.82) (-3.19)

EMU 0.55*** 0.06 0.76***

(3.38) (0.12) (3.92)

Contiguous 0.28 0.39 0.19

(1.53) (0.64) (1.35)

Common official language 1.71*** 5.41*** 1.51***

(4.36) (3.30) (7.44)

Host’s gross expenditure in R&D (% of GDP) 0.59*** 1.06*** 0.71***

(9.15) (4.52) (7.03)

Home’s gross expenditure in R&D (% of GDP) 0.59*** 1.38*** 0.77***

(10.01) (6.60) (11.04)

Host’s diffusion of Internet -0.68*** -2.07** -0.14

(-3.23) (-2.02) (-0.27)

Home’s diffusion of Internet -0.20 -1.11 0.60

(-0.89) (-1.03) (0.76)

Host’s share of highly educated people in total population -0.07 -0.31* -0.04

(-1.01) (-1.82) (-0.78)

Home’s share of highly educated people in total population 0.67*** -0.34 1.58***

(2.99) (-0.46) (3.99)

Income disparity -0.21*** 0.26 0.04

(-3.79) (1.42) (0.55)

Host’s GDP 0.65*** 2.31*** 0.67***

(10.84) (16.06) (8.63)

Home’s GDP 0.65*** 1.95*** 0.79***

(14.51) (14.19) (10.93)

Host’s governance index 0.08*** 0.08 0.04

(2.72) (0.45) (0.47)

Home’s governance index -0.01 0.06 -0.29***

(-0.35) (0.34) (-3.04)

R-squared 0.70 0.59 -

Pseudo R-squared - - 0.87

Number of obs. 392 630 630

Notes: The numbers within parantheses are the t-statistics. The estimations use Eicker-White’s

heteroscedasticity-consistent standard errors. The superscripts (***), (**) and (*) denote

significance at the 1%, 5% and 10% levels, respectively.

30

For other parts of Europe, the differences in languages could be a barrier to ex-

change flows in R&D services. At the national level, each country should invest

in education to prompt people to speak another language than the national one

(English, for instance) in order to facilitate business and transactions between

foreign partners.

Moreover, the level of innovation approximated by the gross domestic expen-

diture in R&D in the host and home countries is significantly and positively

correlated with the offshoring flows of innovation activities. It seems that the

coefficient in the second column is overestimated in comparison with the two

other estimations. The best estimators are likely to come from the PPML

method supported by all the available information. Furthermore, the PPML

estimator of the coefficient of gross domestic expenditure in R&D in the host

country is smaller than the one in the home country. But, principally the more

you do R&D the more you export your expertise in R&D. This is likely to be

linked to the diffusion of new technologies accross Europe.

If we take the example of Eastern European countries, the less developed coun-

tries in Europe, they might offshore their innovation centres in order to create a

channel of knowledge and technology diffusion thanks to their foreign European

partners like Germany, Austria (two favourite locations for offshored R&D ac-

tivities), etc. Hence, this channel may lead to gain knowledge while increasing

the expenses in R&D in the Eastern region and to invest in delocalised R&D

centres. The improvement of innovation in Europe is part of the new objectives

of Europe in 2020 with a 3% share of GDP12 on R&D by easing the access to

venture capital and by promoting more public spending in R&D. This objective

will tend to increase positively R&D offshoring flows and, by a snowball effect,

will spread innovation throughout Europe.

12Innovation priorities for Europe - Presentation of J.M. Barrosso, President of the European

Commission, to the European Council, 4th February 2011.

31

In line with the paper of Márquez-Ramos and Martínez-Zarzoso (2010), the

Internet diffusion variable denotes how well a country is participating in diffus-

ing new technology to acquire knowledge. This factor participates to the level

of innovation in a country. The OLS results in the two first columns appear to

be significant for the level of access to Internet in host country only. The sign

of the relation is negative which can be explained by the fact that, the more a

country is well-equipped with recent technology, the less it is likely to offshore

its innovation activities because it has sufficient knowledge to do research on its

own. Focusing on the third column, we should be cautious and be aware that

the latter observation can be biased or overestimated.

A last variable which is likely to infer on innovation is the available human

skills in science and technology. This is expressed by the proportion of highly

educated people in the total population. Intuitevely, the more we have uni-

versity graduates the more a country can innovate. The education policy is a

major issue during the 21st century because this can determine the future boom

of an economy or maintain a developed economy in the top-rank and even more

so for European countries. The share of highly educated people in a country

hosting offshored innovation activities does not seem to be significant. This

result is constrasting with the views of many authors (see Bunyaratavej et al.,

2007; Deloitte, 2004; Farrell et al., 2006; Lewin and Couto, 2007; Lewin and

Peeters, 2006; Manning et al., 2008) that a large pool of highly skilled workers

is a key strategic driver for offshoring in emerging countries such as India and

China. On the other hand, the coefficient estimated for the same variable in the

home country benefiting from foreign partner’s services in R&D appears to be

highly relevant. Such a positive relationship can be explained by the fact that a

well-educated population in the home country constitutes a required condition

to offshore more towards foreign locations. In fact, a home company which has

offshored its innovation activities to another European country like Germany,

the host country, needs well-qualified people to continue the development after

receiving the results from the offshored R&D department.

32

GDP, which denotes the size of each partner appears to be relevant. This re-

sults is in line with the previous analysis in Section 4.2 where we classified the

countries of our sample under two dimensions: their size and their offshoring

inflows performance. Table 2 (see Appendix C) provides the summary of this

classification and large countries such as Germany have a top performance in

terms of services in R&D. However, the only exception to this principle is Aus-

tria, a small country with respect to GDP, performs better compared to other

large countries like France. Despite this exception, our results fit the classical

statement from the gravity model, the larger you are the more you exports.

Being a big economy attracts more offshoring flows into your borders.

Regarding the governance index based on the six Kaufmann indicators of the

quality of institutions, the only one which is significantly and negatively corre-

lated to the dependent variable is the home country’s governance index. The

improvement of governance in a country generally permits the increase of trade

exchanges with the rest of the world (see Dollar and Kray (2003)). However, for

R&D offshoring flows, such an improvement seems to inhibit a home company

to offshore abroad. Indeed, it will prefer to benefit from the improvement of

the business environment in its national market and keep all its assets in its

headquarters.

5.2 Robustness

5.2.1 Testing

In this section, we conduct some robustness checks in order to test if our model

is well-specified. Firstly, a variance inflation factors (VIF) test is conducted on

the three estimation models in order to measure the multicollinearity. This test

provides an index that measures by how much the variance of an estimated re-

gression coefficient goes up due to the correlations across explanatory variables.

The results show that the multicollinearity is relatively weak i.e. none of the

VIF indexes are excessively high (not greater than 10). For a more precise view,

33

Table 11 and 12 (see Appendix C) exhibit the correlation matrix between ex-

planatory variables. Despite a few correlation coefficients greater than 0.50, this

matrix confirms the result from the VIF test i.e. a relative low multicollinear-

ity among independent variables. Another test called linktest and available in

STATA is used to test specification errors. This test allows to check that, if

the model is properly specified, one should not be able to find any additional

regressors which are statistically significant unless there is a misspecification of

the model such as an omitted relevant variable. The output of this test indicates

a misspecification of the PPML estimation method.

5.2.2 Checking by bloc of countries

Finally, we would like to check if the determinants of R&D offshoring flows are

the same by comparing with a new estimation by bloc of countries through OLS,

transformed-OLS and PPML. These blocs of countries are Western European

countries (WEC: Austria, Denmark, France, Germany and The Netherlands),

Southern European countries (SEC: Cyprus, Greece and Italy) and Central and

Eastern European countries (CEEC: Bulgaria, Czech Repuplic, Latvia, Lithua-

nia, Poland, Romania and Slovakia).

Distance is still a highly relevant factor of offshoring flows by blocs, except

for the CEEC bloc. Looking at the last column of Table 8 and 9 (see Appendix

C), the difference with the general results in Table 1 is that the cost of transpo-

ration (proxied by distance) has a higher impact on flows between WEC bloc as

well as SEC bloc and the rest of Europe. In the case of Southern Europe, the

distance has a large negative relationship with offshoring flows. In constrast,

contiguity does not seem to be a positive factor for transfer from West and

East to other nations. It is inconsistent with the previous result and it may be

due to misspecification in the model. Table 9 (see Appendix C) infers that the

gross expenditure inside the Southern bloc plays a negative role in the offshoring

process. Companies will not outsource their R&D department in the South if

34

this region augments its expenditure in research and development. The rest of

Europe prefers to offshore in Southern locations when there is a clear gap in

terms of innovation. In other words, the South attracts more offshoring flows

by maintaining a difference in innovation level between her and other European

countries.

Besides, Eastern and Western Europe seems to offshore more when their level

of innovation increase as well. This is in line with general empirical results

and it supports the fact that there is a reciprocal knowledge transfer between

West and East. The coefficient of home’s gross expenditure in R&D variable

for the Southern bloc is not consistent with what we explain in the previous

paragraph because of a sample for the South which is probably not represen-

tative. However, for firms which would like to offshore in the Southern bloc,

the web penetration i.e. the fixed lines equipment in this region play a positive

role. Extending this result, we can assume that a Southern European nation

possessing a well-developed infrastructure (power lines, broadband connexion,

etc.) prompts more enterprises to set up their innovation centres within its

borders. Although a country may tend to provide more services in R&D if it

is fully equipped, we note a contrast with Eastern Europe. The infrastructure

improvement in this part of Europe is a negative factor for R&D offshoring.

The estimator for the coefficient of Internet penetration in a home country from

which flows R&D offshoring to Eastern Europe has a relevant positive impact.

Both results for host and home infer that R&D offshoring happens when two

countries (host as part of Eastern bloc and home as part of the rest of Europe)

are largely different with respect to infrastructure levels.

Looking at the HRST13, the only significant results are from Table 8 and 9

(see Appendix C) for respectively WEC and SEC. The higher the well-educated

population in these two blocs, the lower is the offshoring flux. A foreign firm

will prefer to keep control of knowledge in its organisation and not to diffuse13Human resources in Science and Technology.

35

it in order to avoid imitation or industrial espionage. As a general empirical

result, the foreign partner tends to offshore more its R&D department in the

South of Europe when its population of researchers and engineers rises. Such

a fact is probably linked to the previous i.e. keeping control of knowledge and

information about developing products along the value chain. For example, a

company may ask its subsidiary or external partner in the South to develop a

new product but the final step in designing it occurs at home to prevent from

knowledge spread and/or imitation.

Except for Southern nations providing services in R&D, the bigger your are

the more you attract offshoring flows. With respect to income disparity, the

most significant results go to the Southern and Eastern European regions. It

is clear that the difference in income level between partners is not positively

correlated to offshoring flows in these blocs. Consequently, a foreign firm may

decide to offshore more in a country from these areas if it has a similar level of

income. The quality of institutions is positively correlated to offshoring flows to

the West and the contrary to the South. By the way, a foreign partner is more

likely to build an affiliate or a relationship with an external partner in the West

of Europe when legal conditions constitutes an advantage. For the Southern

bloc, it is the reverse. However, good governance in the home country influ-

ences domestic companies to offshore much more in the South. This might be

due to a too restrictive business environment. The partner in the home country

will offshore its R&D activity to prevent such a situation and to benefit from a

more permissive or corrupted state.

36

6 Conclusion

As Amiti and Wei (2005) said, the innovation-related activities tend to be diffi-

cult to offshore because they imply an important risk burden for domestic firms

and also intangible assets such as knowledge, skills, education, etc. For the same

reasons, the determinants of R&D offshoring flows in Europe are relatively diffi-

cult to find. Indeed, the principal reason to offshore for a company is often not

the same for another because such a decision is linked to different strategies. If,

at the company level, is not easy to highlight the causes of offshoring, it could

be easier to find them in a more aggregated view. However, from this point of

view, the tough element is to get an offshoring measure from which we can infer

some results. This study focuses on R&D offshoring flows between European

nations and uses a proxy to measure these flows. A gravity model is built to

assess the relationship between our variable of interest and different factors.

The main findings of this study are that a firm prefers to offshore in a close

location in order to lower the cost of transportation and even more to keep

easily control on its foreign assets or foreign partners. The fact that two part-

ners share the same currency constitutes an advantage which prompts firms to

offshore more. Also, the fact that two European countries have a common offi-

cial language is positively correlated to the R&D offshoring flows between them.

Hence, being close to each other in terms of distance and culture are determinis-

tic factors which tend to influence which region a company will choose either to

install an offshored subsidiary or to contract with a foreign arm’s length partner

to do R&D activities. Principally, the more two partners do R&D (increasing

gross expenditure in R&D), the more they exports their services in R&D. At the

level of Western and Eastern European blocs, the gross expenditure in R&D in

each bloc has a similar impact on offshoring which might show the existence of a

reciprocal knowledge transfer among these parts of Europe. So, R&D offshoring

tends to spread innovation throughout Europe and can be a positive factor for

future growth within the European Union.

37

Another implication from this study is to drive Southern nations to invest more

in their infrastructures (power lines, broadband connexions, etc.) in order to

attract more companies to set up R&D assets within their national borders.

Such a policy will promote diffusion of new technology and increase innovation

in the South of Europe. Looking at the available skills in R&D, in contradic-

tion with previous studies, the share of highly educated people in a country

hosting offshored innovation activities seems not to be significant. Despite this

general result, at an aggregated level, in the WEC and SEC blocs, the higher

the well-educated population, the less they provide services in R&D. Unless

keeping at home the final step of research and development, a foreign firm will

prefer to keep control of knowledge and information within its organisation and

not to diffuse it along the value chain in order to avoid imitation or industrial

espionage. This means that a foreign firm prefers to maintain a certain depen-

dence of its offshored assets by retaining an essential and complex element in the

R&D process in its headquarters. The “secret recipe” of a company necessary

to complete the process of development is kept at home whereas the rest and

less complex part of the same process is done in foreign locations.

In line with the fact that the more two partners are close in terms of distance

and culture, the more they trade together, the level of income plays a similar

role. Neighbouring nations such as Austria and Germany will exchange more

services in R&D thanks to their similarities (level of income, culture, language,

etc.) than completely different nations would do. A good quality of institutions

constitutes an advantage for Western countries. Indeed, a foreign partner is

more likely to build an affiliate or a relationship with an external partner in

Western Europe when legal conditions are favourable to do business.

In light of these results, we can recommend some policy implications at the

European level to improve the business environment and to promote the intra-

European offshoring of innovation-related activities:

38

1. Enlarge the Eurozone to more countries to ease the transactions between

a parent company and its affiliates;

2. At the national level, invest more money into the language education at the

primary and secondary level in order to have a larger European population

who can speak several different languages (e.g. English);

3. Prompt the national public sector to spend more in R&D;

4. Facilitate the access to venture capital in order to have more private in-

vestment in R&D;

5. Drive Southern European nations to improve their infrastructure level

(power lines, broadband connexions, etc.) to attract more R&D offshoring

flows and increase the innovation in these regions;

6. Invest in education at the university level to increase the population of

researchers and engineers;

7. Create a financial or fiscal incentive at the EU level to convince firms to

offshore completely their R&D activity and not to retain a part of that at

home;

8. Improve the quality of institutions throughout Europe to have the best

possible business environment and avoid any complications linked to a

poor level of governance

Unfortunately, such findings do not have the presumption to be the most rele-

vant ones about offshoring of R&D services in Europe. Future research should

expand such a model more broadly at the international level by collecting data

on offshoring flows between major economies such as Europe, USA, China, In-

dia, and BRICs countries. Indeed, the factors explaining these flows are likely

to be somewhat different compared to intra-European factors. The competition

on taxation regimes between countries could really be a relevant factor for study

in the case of R&D offshoring and may imply a new policy at the international

39

level to regulate this competition and improve the conditions for offshoring.

Moreover, one needs to be cautious on these findings because a part of our in-

ference through PPML is not robust caused in part by omitted variables. In

addition, our results are based on a proxy which is not only linked to offshoring

of R&D services. Consequently, one needs to have a specific accounting item

in the Balance of Payments for transactions by type of products entirely due

to offshoring (e.g. transactions between a parent company and its foreign sub-

sidiaries). In this way, there will be numerous other studies on the topic by

including and testing more other explanatory variables and, hence, to produce

more consistent and interesting results.

40

References

Offshore bonanza: Smart firms look beyond mere cost savings. Strategic Di-

rection, 22 Iss: 5:13–15, 2006.

Mary Amiti and Shang-Jin Wei. Fear of Service Outsourcing: Is it Justified?

IMF Working Papers 04/186, International Monetary Fund, October 2004.

James E. Anderson. A theoretical foundation for the gravity equation. Amer-

ican Economic Review, 69(1):106–16, March 1979.

Pol Antras and Elhanan Helpman. Global sourcing. Journal of Political Econ-

omy, 112(3):552–580, June 2004.

Ashok Deo Bardhan. Managing globalization of R&D: Organizing for off-

shoring innovation. Human Systems Management, 25:103–114, 2006.

Ashok Deo Bardhan and Dwight M. Jaffee. Innovation, R&D and offshoring.

Technical report, UC Berkeley: Fisher Center for Real Estate and Urban Eco-

nomics, 2005.

S. Lael Brainard. An empirical assessment of the proximity-concentration

trade-off between multinational sales and trade. American Economic Review,

87(4):520–44, September 1997.

Lorena M. D’Agostino, Keld Laursen, and Grazia Santangelo. The impact of

R&D offshoring on the home knowledge production of oecd investing regions.

DRUID Working Papers 10-19, DRUID, Copenhagen Business School, Depart-

ment of Industrial Economics and Strategy/Aalborg University, Department

of Business Studies, 2010.

Henri L.F. de Groot, Gert-Jan Linders, Piet Rietveld, and Uma Subramanian.

The institutional determinants of bilateral trade patterns. Tinbergen Institute

Discussion Papers 03-044/3, Tinbergen Institute, June 2003.

41

Felipa de Mello-Sampayo. Competing-destinations gravity model: An applica-

tion to the geographic distribution of FDI. Applied Economics, 41(17):2237–

2253, 2009.

Ruud A. de Mooij and Sjef Ederveen. What a difference does it make? under-

standing the empirical literature on taxation and international capital flows.

European Commission - Directorate-General for Economic and Financial

Affairs, 2006. Paper prepared for the workshop of DG ECFIN of the Euro-

pean Commission on Corporate tax competition and coordination in Europe,

September 25th, 2006, Brussels.

Alan Deardorff. Determinants of bilateral trade: Does gravity work in a neo-

classical world? In The Regionalization of the World Economy, NBER Chap-

ters, pages 7–32. National Bureau of Economic Research, Inc, 1998.

David Dollar and Aart Kraay. Institutions, trade, and growth. Journal of

Monetary Economics, 50(1):133–162, January 2003.

Rafiq Dossani and Arvind Panagariya. Globalization and the offshoring of

services: The case of India. Brookings Trade Forum, Offshoring White-Collar

Work:241–277, 2005.

Jonathan Eaton and Samuel Kortum. Technology, geography, and trade.

Econometrica, 70(5):1741–1779, September 2002.

Hartmut Egger and Peter Egger. International outsourcing and the produc-

tivity of low-skilled labor in the eu. Economic Inquiry, 44(1):98–108, January

2006.

Carsten Fink, Aaditya Mattoo, and Ileana Cristina Neagu. Assessing the im-

pact of communication costs on international trade. Journal of International

Economics, 67(2):428–445, December 2005.

Jeffrey A. Frankel and David Romer. Does trade cause growth? American

Economic Review, 89(3):379–399, June 1999.

42

William H. Greene. Econometric analysis. Prentice Hall, 5th edition, 2005.

Elhanan Helpman. A simple theory of international trade with multinational

corporations. Journal of Political Economy, 92(3):451–71, June 1984.

Elhanan. Helpman and Paul R. Krugman. Market structure and foreign trade

: increasing returns, imperfect competition, and the international economy /

Elhanan Helpman and Paul R. Krugman. MIT Press, Cambridge, Mass. :,

1985.

Daniel Kaufmann, Aart Kraay, and Massimo Mastruzzi. The worldwide gover-

nance indicators : methodology and analytical issues. Policy Research Working

Paper Series 5430, The World Bank, September 2010.

Fukunari Kimura and Hyun-Hoon Lee. The gravity equation in international

trade in services. Review of World Economics (Weltwirtschaftliches Archiv),

142(1):92–121, April 2006.

Mark Knell and Matija Rojec. European offshoring: where and whence. Tech-

nical report, European Trade Study Group (ETSG), August 2009.

Arie Lewin, Silvia Massini, and Carine Peeters. Why are companies offshoring

innovation ? The emerging global race for talent. Working Papers CEB 08-

009.RS, ULB – Université Libre de Bruxelles, March 2008.

. Linnemann, Hans. An econometric study of international trade flows / Hans

Linnemann. North-Holland, Amsterdam :, 1966.

Prakash Loungani, Ashoka Mody, and Assaf Razin. The global disconnect:

The role of transactional distance and scale economies in gravity equations.

Scottish Journal of Political Economy, 49(5):526–43, December 2002.

Stephan Manning, Silvia Massini, and Arie Y. Lewin. A Dynamic Perspective

on Next-Generation Offshoring: The global Sourcing of Science and

Engineering Talent. Academy of Management Perspectives, 22(3):35–54, Oc-

tober 2008.

43

Inmaculada Martínez-Zarzoz and Felicitas Nowak Lehmann. Augmented grav-

ity model: An empirical application to Mercosur-European union trade flows.

Journal of Applied Economics, VI:291–316, 2003.

Thierry Mayer and Soledad Zignago. Notes on CEPIIs distances measures,

May 2006.

Pierre-Guillaume Méon and Anne-France Delannay. The impact of European

integration on the nineties’ wave of mergers and acquisitions. ULB Institutional

Repository 2013/8366, ULB – Universite Libre de Bruxelles, September 2006.

Pierre-Guillaume Méon and Khalid Sekkat. Institutional quality and trade:

which institutions? which trade? DULBEA Working Papers 06-06.RS, ULB

– Universite Libre de Bruxelles, April 2006.

Laura Márquez-Ramos and Inmaculada Martínez-Zarzoso. The effect of tech-

nological innovation on international trade. A nonlinear approach. Economics:

The Open-Access, Open-Assessment E-Journal, 4:1–5, 2010.

Alireza Naghavi and Gianmarco Ireo Paolo Ottaviano. Offshoring and prod-

uct innovation. CEPR Discussion Papers 6008, C.E.P.R. Discussion Papers,

December 2006.

Organisation for Economic Co-operation and Development (OECD), Paris.

Main Definitions and Conventions for the Measurement of Research and Ex-

perimental Development (R&D). A Summary of the Frascati Manual 1993,

1994.

Tiiu Paas, Egle Tafenau, and Nancy J. Scannell. Gravity equation analysis in

the context of international trade: Model specification implications in the case

of the European Union. Eastern European Economics, 46(5):92–113, Septem-

ber 2008.

P. Pöyhönen. A tentative model for the volume of trade between countries.

Weltwirtschaftliches Archiv, 90:93–99, 1963.

44

Åsa Hansson and Karin Olofsdotter. Foreign Direct Investment in Europe:

Tax competition and agglomeration economies. Technical report, European

Trade Study Group (ETSG), August 2008.

Joao Santos Silva and Silvana Tenreyro. The log of gravity. CEP Discussion

Papers dp0701, Centre for Economic Performance, LSE, July 2005.

Marie Stack. Regional integration and trade: Controlling for varying degrees

of heterogeneity in the gravity model. World Economy, 32:772–789, 2009.

Jan Tinbergen. Shaping the world economy : suggestions for an international

economic policy. Twentieth Century Fund, N.Y. :, 1962.

Ainura Uzagalieva, Evzen Kocenda, and Antonio Menezes. Technological imi-

tation and innovation in new European Union markets. CESifo Working Paper

Series, 3039:1–14, 2010.

Desirée van Welsum and Xavier Reif. Potential offshoring: Evidence from

selected OECD countries. Brookings Trade Forum, Offshoring White-Collar

Work:165–194, 2005.

45

Appendix

Appendix A: Silva and Teneyro’s model

As suggested by the economic models and Greene (2005), the gravity equation

predicts the expected value of variable of interest, y ≥ 0, for a given value

of the explanatory variable, x. Silva and Teneyro take a constant-elasticity

model of the form yi = exp(xiβ) as suggested by the economic theory and it

is interpreted as the conditionnal expectation of yi given x, denoted E[yi|x].

Because of the fact a such relation is impossible to hold for each i, there is an

error-term associated to it. So, let assume that the stochastic model is defined

by the following expression:

yi = exp(xiβ) + εi, (4)

with yi ≥ 0 and E[εi|x] = 0. The previous equation can be written as following:

yi = exp(xiβ)ηi, (5)

where ηi = 1 + εi/exp(xiβ) and E[ηi|x] = 1. Assuming that yi is positive, the

model can be linearised by taking logs:

ln(yi) = xiβ + ln(ηi), (6)

where ln(E[ηi|x]) = 0; E[ln(ηi)|x]) 6= 0. To estimate this equation while con-

trolling heteroscedasticity, Silva and Teneyro propose the pseudo maximum like-

lihood estimator by assuming that the conditional variance is proportional to

the conditional mean, E[yi|x] = exp(xiβ) ∝ V [yi|x], and β can be estimated by

solving the following set of first-order conditions:

Σni=1[yi − exp(xiβ̃)]xi = 0 (7)

As we can see, the estimator defined by equation (7) is numerically equal to the

Poisson Pseudo-Maximum Likelihood (PPML) estimator, which is often used

for count data. However, as the authors said in their paper, the “data do not

have to be Poisson at all - and, what is more important, yi does not even have to

46

be an integer - for the estimator based on the Poisson likelihood function to be

consistent. This is the well-known PML result first noted by Gourieroux, Mon-

fort, and Trognon (1984)”. The required condition for the estimator expressed

in equation (7) to be consistent is the correct specification of the conditional

mean E[yi|x] = exp(xiβ). As explained by Silva and Teneyro, the assumption

that the conditional variance is proportional to the conditional mean is unlikely

to hold, this estimator does not take full account of the heteroskedasticity in

the model, and consequently all inference has to be based on an Eicker-White

robust covariance matrix estimator.

47

Appendix B: Figures

Figure 1: Products and occupations: the firm matrix

48

Figure 2: Selected countries

Austria Czech Repuplic Germany Latvia Poland

Bulgaria Denmark Greece Lithuania Romania

Cyprus France Italy The Netherlands Slovakia

49

Figure 3: Share of each European bloc in the R&D offshoring inflows on average

Note: WEC: Western European countries; SEC: Southern European countries;

CEEC: Central and Eastern European countries.

Source: Own calculations.

50

Figure 4: Highly educated population and gross domestic expenditure in R&D

on average over the period 2007-2009

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic;

DEU: Germany; DNK: Denmark; FRA: France; GRC: Greece; ITA: Italy;

LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM:

Romania; SVK: Slovakia.

Source: Own calculations.

51

Figure 5: Relation between the weight in the sample of country’s size and

offshoring inflows

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic;

DEU: Germany; DNK: Denmark; FRA: France; GRC: Greece; ITA: Italy;

LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM:

Romania; SVK: Slovakia.

Source: Own calculations.

52

Appendix C: Tables

Table 2: Classification of countriesOffshoring inflows performance

Size High Middle Low

High DEU FRA

Middle ITA GRC

Low AUT NLD Others

Notes: DEU: Germany; AUT: Austria;

FRA: France; ITA: Italy; NLD: Netherlands;

GRC: Greece; Others: Poland, Denmark,

Latvia, Lithuania, Cyprus, Romania,

Slovakia, Czech Republic and Buglaria.

Source: Own calculations.

53

Table 3: Kaufmann indicators of governance

1.‘Voice and accountability’ captures perceptions of the extent to which a country’s

citizens are able to participate in selecting their government, as well as freedom of

expression, freedom of association, and a free media.

2.‘Political stability’ and absence of violence measures the perceptions of the like-

lihood that the government will be destabilized or overthrown by unconstitutional

or violent means, including domestic violence and terrorism.

3.‘Government effectiveness’ captures perceptions of the quality of public services,

the quality of the civil service and the degree of its independence from political

pressures, the quality of policy formulation and implementation, and the credibility

of the government’s commitment to such policies.

4.‘Regulatory quality’ captures perceptions of the ability of the government to

formulate and implement sound policies and regulations that permit and promote

private sector development.

5.‘Rule of law’ captures perceptions of the extent to which agents have confidence

in and abide by the rules of society, and in particular the quality of contract

enforcement, property rights, the police, and the courts, as well as the likelihood

of crime and violence.

6.‘Control of corruption’ captures perceptions of the extent to which public power

is exercised for private gain, including both petty and grand forms of corruption,

as well as “capture” of the state by elites and private interests.

Source: Kaufmann D., A. Kraay, and M. Mastruzzi (2010), The Worldwide Gov-

ernance Indicators: Methodology and Analytical Issues.

54

Table 4: Means of R&D offshoring flows in current euros by pair of countries

over the period 2007-2009Country j

Country i AUT BGR CYP CZE DEU DNK FRA GRC

AUT . 0.00E+00 0.00E+00 3.33E+06 8.55E+08 1.33E+06 2.53E+07 6.67E+05

BGR 4.23E+06 . 0.00E+00 0.00E+00 2.57E+06 0.00E+00 2.03E+06 1.67E+05

CYP 0.00E+00 0.00E+00 . 0.00E+00 4.50E+06 0.00E+00 3.47E+06 6.47E+06

CZE 4.03E+06 1.00E+05 0.00E+00 . 2.45E+07 1.00E+06 1.02E+07 0.00E+00

DEU 8.00E+07 1.33E+06 3.33E+05 1.47E+08 . 4.43E+07 5.80E+08 8.67E+06

DNK 3.33E+05 1.00E+05 0.00E+00 3.33E+05 1.45E+07 . 7.63E+06 2.33E+05

FRA 9.00E+06 4.00E+06 3.00E+06 7.33E+06 2.60E+08 1.00E+07 . 6.00E+06

GRC 1.50E+06 3.00E+05 3.13E+06 3.00E+05 1.28E+07 9.00E+05 7.67E+06 .

ITA 5.67E+06 1.00E+06 0.00E+00 0.00E+00 1.04E+08 2.33E+06 6.13E+07 4.67E+06

LTU 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.80E+06 0.00E+00 2.33E+05 0.00E+00

LVA 2.83E+06 0.00E+00 0.00E+00 0.00E+00 1.40E+06 0.00E+00 0.00E+00 0.00E+00

NLD 3.17E+07 0.00E+00 0.00E+00 1.92E+06 2.58E+08 1.05E+07 1.16E+08 2.03E+06

POL 2.70E+06 3.00E+05 0.00E+00 9.00E+05 3.22E+07 1.77E+06 1.29E+07 4.67E+05

ROM 1.10E+06 6.33E+05 7.33E+05 1.77E+06 1.60E+07 1.00E+06 5.03E+06 4.33E+05

SVK 1.10E+06 1.33E+05 0.00E+00 5.50E+06 4.90E+06 0.00E+00 2.67E+05 0.00E+00

Total 1.44E+08 7.90E+06 7.20E+06 1.68E+08 1.59E+09 7.32E+07 8.32E+08 2.98E+07

Share (in %) 3.69 0.20 0.18 4.31 40.82 1.87 21.31 0.76

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic; DEU: Germany; DNK: Denmark; FRA: France;

GRC: Greece; ITA: Italy; LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM: Romania; SVK: Slovakia.

Source: Own calculations.

Table 5: Means of R&D offshoring flows in current euros by pair of countries

over the period 2007-2009 (continued)Country j

Country i ITA LTU LVA NLD POL ROM SVK Total Share (in %)

AUT 3.60E+07 0.00E+00 0.00E+00 3.53E+07 1.67E+06 1.00E+06 3.33E+06 9.63E+08 24.67

BGR 5.00E+05 0.00E+00 0.00E+00 6.67E+05 0.00E+00 1.67E+05 0.00E+00 1.03E+07 0.26

CYP 0.00E+00 0.00E+00 0.00E+00 1.00E+05 0.00E+00 0.00E+00 0.00E+00 1.45E+07 0.37

CZE 3.33E+05 0.00E+00 0.00E+00 8.50E+06 2.33E+05 0.00E+00 1.73E+06 5.06E+07 1.30

DEU 1.54E+08 3.33E+06 0.00E+00 1.47E+08 2.27E+07 5.67E+06 1.17E+07 1.21E+09 30.87

DNK 3.03E+06 3.20E+06 3.33E+04 1.94E+07 6.33E+05 2.33E+05 1.00E+05 4.98E+07 1.27

FRA 7.83E+07 1.00E+06 1.00E+06 1.59E+08 6.33E+06 9.33E+06 2.33E+06 5.57E+08 14.26

GRC 6.27E+06 0.00E+00 1.67E+05 3.50E+06 9.33E+05 6.00E+05 3.00E+05 3.84E+07 0.98

ITA . 0.00E+00 0.00E+00 1.75E+08 1.33E+06 6.67E+05 0.00E+00 3.56E+08 9.11

LTU 0.00E+00 . 0.00E+00 0.00E+00 3.67E+05 0.00E+00 0.00E+00 3.40E+06 0.09

LVA 0.00E+00 0.00E+00 . 0.00E+00 0.00E+00 0.00E+00 0.00E+00 4.23E+06 0.11

NLD 1.33E+07 0.00E+00 3.33E+04 . 6.93E+07 2.33E+05 7.00E+05 5.04E+08 12.91

POL 6.43E+06 3.67E+05 2.00E+05 3.62E+07 . 2.67E+05 9.33E+05 9.56E+07 2.45

ROM 2.40E+06 0.00E+00 3.00E+05 5.00E+06 3.00E+05 . 0.00E+00 3.47E+07 0.89

SVK 4.33E+05 3.33E+04 6.67E+04 1.00E+05 5.27E+06 3.33E+05 . 1.81E+07 0.46

Total 3.01E+08 7.93E+06 1.80E+06 5.89E+08 1.09E+08 1.85E+07 2.11E+07 3.90E+09

Share (in %) 7.71 0.20 0.05 15.08 2.79 0.47 0.54 100.00

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic; DEU: Germany; DNK: Denmark; FRA: France;

GRC: Greece; ITA: Italy; LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM: Romania; SVK: Slovakia.

Source: Own calculations.

55

Table 6: Means of highly educated population over the period 2007-2009Country Highly educated population on average Share in the sample (in %)

BGR 974.67 2.30

CZE 923.23 2.18

DNK 982.37 2.32

DEU 11498.00 27.11

GRC 1421.57 3.35

FRA 9986.60 23.54

ITA 4898.53 11.55

CYP 159.87 0.38

LVA 641.90 1.51

LTU 578.37 1.36

NLD 3005.17 7.08

AUT 854.77 2.02

POL 4408.67 10.39

ROM 1595.57 3.76

SVK 488.87 1.15

Total 42418.13 100.00

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic;

DEU: Germany; DNK: Denmark; FRA: France; GRC: Greece; ITA: Italy;

LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM: Romania;

SVK: Slovakia.

Source: Own calculations.

56

Table 7: Means of gross domestic expenditures in R&D in current euros over

the period 2007-2009Country Gross domestic expenditure in R&D on average Share in the sample (in %)

AUT 7.30E+09 4.72

BGR 1.60E+08 0.10

CYP 7.40E+07 0.05

CZE 2.10E+09 1.36

DEU 6.50E+10 42.06

DNK 6.40E+09 4.14

FRA 4.00E+10 25.88

GRC 1.30E+09 0.84

ITA 1.90E+10 12.29

LTU 2.40E+08 0.16

LVA 1.20E+08 0.08

NLD 1.00E+10 6.47

POL 1.90E+09 1.23

ROM 6.70E+08 0.43

SVK 2.80E+08 0.18

Total 1.55E+11 100.00

Notes: AUT: Austria; BGR: Bulgaria; CYP; Cyprus; CZE; Czech Republic;

DEU: Germany; DNK: Denmark; FRA: France; GRC: Greece; ITA: Italy;

LTU: Lithuania; LVA: Latvia; NLD: Netherlands; POL: Poland; ROM: Romania;

SVK: Slovakia.

Source: Own calculations.

57

Table 8: Empirical results for Western European countries blocWEC OLS OLS PPML

Dependent variable log(Offijt) log(1+Offijt) Offijt

Distance -0.76*** -0.32 -0.77***

(-4.35) (-0.45) (-3.35)

EMU 0.22 -0.24 -0.21

(0.85) (-0.30) (-0.64)

Contiguous 0.45 0.15 0.28

(1.48) (0.16) (1.00)

Common official language - - -

Host’s gross expenditure in R&D (% of GDP) -0.73 2.26 -2.73

(-0.88) (0.57) (-1.43)

Home’s gross expenditure in R&D (% of GDP) 0.79*** 1.10*** 0.74***

(9.03) (3.85) (7.23)

Host’s diffusion of Internet -0.72 -1.32 -4.27

(-1.25) (-0.13) (-1.24)

Home’s diffusion of Internet -0.46 1.72 0.71

(-1.26) (0.93) (1.18)

Host’s share of highly educated people in total population -0.17* -0.63** -0.19*

(-1.95) (-2.13) (-1.74)

Home’s share of highly educated people in total population 0.66 -5.92*** -0.27

(1.54) (-4.15) (-0.52)

Income disparity -0.19 1.43** -0.15

(-1.41) (2.25) (-0.92)

Host’s GDP 1.22*** 2.05 1.93***

(8.82) (1.42) (3.65)

Home’s GDP 0.74*** 1.74*** 0.73***

(7.35) (6.12) (5.10)

Host’s governance index -0.03 -0.34 0.68*

(-0.74) (-0.31) (1.73)

Home’s governance index -0.02 0.22 -0.02

(-0.41) (0.89) (-0.19)

R-squared 0.74 0.64 -

Pseudo R-squared - - 0.81

Number of obs. 114 150 150

Notes: The numbers within parantheses are the t-statistics. The estimations use Eicker-White’s

heteroscedasticity-consistent standard errors. The superscripts (***), (**) and (*) denote

significance at the 1%, 5% and 10% levels, respectively.

58

Table 9: Empirical results for Southern European countries blocSEC OLS OLS PPML

Dependent variable log(Offijt) log(1+Offijt) Offijt

Distance -2.52*** -5.96** -4.95***

(-4.57) (-2.33) (-6.67)

EMU 0.85*** -1.88 1.22***

(2.65) (-1.48) (3.19)

Contiguous -1.10*** 1.25 -0.99***

(-2.78) (0.73) (-3.31)

Common official language - - -

Host’s gross expenditure in R&D (% of GDP) -8.81*** -1.16 -13.27***

(-3.57) (-0.20) (-3.18)

Home’s gross expenditure in R&D (% of GDP) -0.21 -1.13 -1.70***

(-0.69) (-0.65) (-2.88)

Host’s diffusion of Internet 1.41 3.52 9.43***

(1.06) (0.61) (2.53)

Home’s diffusion of Internet 0.91 -3.52 -0.95

(1.46) (-0.95) (-0.66)

Host’s share of highly educated people in total population -55.13*** 6.62 -72.16***

(-3.70) (0.20) (-3.18)

Home’s share of highly educated people in total population 1.99*** 5.10*** 6.40***

(3.93) (2.96) (10.10)

Income disparity -0.62*** -1.03 -0.64**

(-4.00) (-1.55) (-2.14)

Host’s GDP -8.38*** -1.52 -12.91***

(-3.58) (-0.28) (-3.21)

Home’s GDP 0.90*** 3.48*** 1.18***

(6.58) (7.10) (5.91)

Host’s governance index 0.08 -4.38*** -1.72**

(0.83) (-3.52) (-2.21)

Home’s governance index -0.01 0.88 0.78***

(-0.10) (1.17) (2.75)

R-squared 0.90 0.71 -

Pseudo R-squared - - 0.94

Number of obs. 57 108 108

Notes: The numbers within parantheses are the t-statistics. The estimations use Eicker-White’s

heteroscedasticity-consistent standard errors. The superscripts (***), (**) and (*) denote

significance at the 1%, 5% and 10% levels, respectively.

59

Table 10: Empirical results for Central and Eastern European countries blocCEEC OLS OLS PPML

Dependent variable log(Offijt) log(1+Offijt) Offijt

Distance -0.12 -3.94*** -0.62

(-0.44) (-4.42) (-1.58)

EMU -0.93*** -1.28 -1.31***

(-1.65) (-0.70) (-3.48)

Contiguous 0.81 -3.41** -0.52

(1.77) (2.19) (-1.52)

Common official language - - -

Host’s gross expenditure in R&D (% of GDP) -0.01 -0.05 -0.11

(-0.03) (-0.05) (-0.51)

Home’s gross expenditure in R&D (% of GDP) 0.32*** 0.84* 0.34*

(2.17) (1.83) (1.85)

Host’s diffusion of Internet -0.85 -2.88* -0.93***

(-1.78) (-1.82) (-2.49)

Home’s diffusion of Internet 1.46* 1.95 3.96***

(2.79) (0.68) (3.75)

Host’s share of highly educated people in total population 0.00 -0.19 -0.03

(0.02) (-0.87) (-0.36)

Home’s share of highly educated people in total population 0.35 0.36 0.77

(0.54) (0.14) (0.89)

Income disparity -0.32 -1.15 -1.26**

(-1.30) (-1.38) (-2.19)

Host’s GDP 0.83*** 3.76*** 1.30***

(4.95) (10.12) (8.73)

Home’s GDP 0.45*** 1.81*** 0.60***

(4.26) (6.45) (7.03)

Host’s governance index 0.07 -0.60*** -0.03

(1.06) (-2.61) (-0.51)

Home’s governance index 0.04 -0.09 -0.13

(0.86) (-0.16) (-0.80)

R-squared 0.52 0.65 -

Pseudo R-squared - - 0.80

Number of obs. 103 168 168

Notes: The numbers within parantheses are the t-statistics. The estimations use Eicker-White’s

heteroscedasticity-consistent standard errors. The superscripts (***), (**) and (*) denote

significance at the 1%, 5% and 10% levels, respectively.

60

Table 11: Correlation matrix1 2 3 4 5 6 7 8

1. Distance 1

2. EMU 0.07 1

3. Contiguous -0.66* 0.12* 1

4. Common official language -0.09* 0.22* 0.13* 1

5. Host’s gross expenditure in R&D (% of GDP) -0.1* 0.16* 0.09* -0.01 1

6. Home’s gross expenditure in R&D (% of GDP) -0.1* 0.16* 0.09* -0.01 -0.07 1

7. Host’s diffusion of Internet -0.11* 0.18* 0.07 0.01 0.49* -0.05 1

8. Home’s diffusion of Internet -0.11* 0.18* 0.07 0.01 -0.05 0.49* 0.02 1

9. Host’s share of highly educated people in total population 0.09* 0 -0.04 0.02 0.03 -0.04 0.05 -0.07

10. Home’s share of highly educated people in total population 0.21* 0 -0.12* 0.02 0.01 -0.12* 0 0.37*

11. Income disparity 0.23* -0.12* -0.29* -0.12* 0.01 0.01 -0.04 -0.04

12. Host’s GDP -0.08* 0.29* 0.13* 0.04 0.23* -0.02 0.35* -0.02

13. Home’s GDP -0.08 0.29* 0.13* 0.04 -0.02 0.23* -0.01 0.35*

14. Host’s governance index -0.07 0.22* 0.07 0.07 0.51* -0.04 0.83* -0.06

15. Home’s governance index -0.07 0.22* 0.07 0.07 -0.04 0.51* -0.06 0.84*

Notes: The superscript (*) means that correlation is significantly different from zero at p<0.05. The number of observations is 630.

Table 12: Correlation matrix (continued)9 10 11 12 13 14 15

1. Distance

2. EMU

3. Contiguous

4. Common official language

5. Host’s gross expenditure in R&D (% of GDP)

6. Home’s gross expenditure in R&D (% of GDP)

7. Host’s diffusion of Internet

8. Home’s diffusion of Internet

9. Host’s share of highly educated people in total population 1

10. Home’s share of highly educated people in total population -0.04 1

11. Income disparity -0.03 -0.02 1

12. Host’s GDP 0.04 0.02 0.12* 1

13. Home’s GDP -0.03 -0.20* 0.13* -0.07 1

14. Host’s governance index 0.10* -0.02 0.08 0.40* -0.03 1

15. Home’s governance index -0.06 0.34* 0.08* -0.03 0.40* -0.07 1

Notes: The superscript (*) means that correlation is significantly different from zero at p<0.05. The number of observations is 630.

61