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  1. 1. MEMOIRE Présenté en vue de lobtention du Master en Sciences économiques, finalité EntreprisesDeterminants 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 A n né e a ca d é m i qu e 2 01 0 - 20 11
  2. 2. Determinants of European R&D offshoring: Agravity 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.
  3. 3. AcknowledgmentsI wish to thank Carine Peeters, my thesis director, for her support during myresearch for this paper and also Julien Gooris who helped me to modelise as bestas 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 lastyear of research and writing.
  4. 4. ContentsAcknowledgments 1Contents 2List of Figures 4List of Tables 41 Introduction 52 A broad overview on offshoring 8 2.1 Offshoring vs. outsourcing . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Literature review and offshoring trends . . . . . . . . . . . . . . . 93 The gravity equation model 13 3.1 The classical gravity equation model . . . . . . . . . . . . . . . . 13 3.2 Empirical background . . . . . . . . . . . . . . . . . . . . . . . . 164 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 . . . . . . . . . . . . . . . . . . . . . . 255 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
  5. 5. 6 Conclusion 37References 41Appendix 46 Appendix A: Silva and Teneyro’s model . . . . . . . . . . . . . . . . . 46 Appendix B: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Appendix C: Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3
  6. 6. 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 52List 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
  7. 7. 1 IntroductionSince the 80s, there have been different waves of manufacturing offshoring allaround 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, thereare several other reasons and advantages such as the access to distinctive skills1and growing performance from fast-developing economies, particularly in Asia.Currently, companies are meeting a new round in the offshoring trend: theyare likely to think about other ways to improve their structures in R&D2 . Themost 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 allcost sources, including R&D and innovation-related activities.More precisely, the international trade theory has a limited consideration asto the effects of offshoring on R&D activities in the country of origin. Certainauthors such as A. Naghavi and G. Ottaviano (2006) tried to fill the gap in theinternational trade literature on the dynamic effects of offshoring on R&D. Theauthors determined that, when offshoring reduces the feedback from offshoredplants to domestic labs, it is likely to bring dynamic losses when the countriesof 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 inducessome coordination problems between the offshored and domestic divisions of acorporation. 1 Skills are likely to be available in abundance. For instance, China produces 350,000engineering graduates each year compared to 90,000 in the U.S.A. - “Offshore bonanza: Smartfirms look beyond mere cost savings”, Strategic Direction (2006), Vol. 22 Issue: 5, pp. 13-15. 2 The following definition of R&D comes from the OECD summary of Frascati Manualwhich helps national experts in OECD countries to collect and issue R&D data: “Researchand experimental development (R&D) comprise creative work undertaken on a systematicbasis in order to increase the stock of knowledge, including knowledge of man, culture andsociety, and the use of this stock of knowledge to devise new applications. R&D is a termcovering three activities: basic research, applied research, and experimental development.” 5
  8. 8. 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 adomestic location a corporation may decide to set up a foreign affiliate whichwill 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 tothe national level.However, companies should take into account the complementarity of home andoffshored R&D activities to achieve a competitive advantage. As D’Agostino etal. (2010) suggested, the complementarity between domestic and foreign inno-vative assets depends on their natures and their complexity. In fact, the homeand offshored R&D activities are complementary if they are not similar as wellas when offshored R&D activity is concerned with modular and less complextechnologies. This finding is based on the geographical technological specialisa-tion and the reverse knowledge transfer from the offshore locations to the homeregions.Moreover, when looking at the structural attributes of R&D offshoring, thereare 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, manufacturingoffshoring influences contiguous and similar skills and occupations within theblue-collar workforce, whereas outsourcing/offshoring of services and R&D af-fects white collar jobs across dissimilar occupations. Manufacturing offshoringcan be viewed as impacting along product lines, whilst, services offshoring isimpacting along occupational/job lines. R&D offshoring is a mix of both. Thedevelopment of a new product would initially be part of manufacturing off-shoring but this kind of activity requires specialised occupations/jobs such as 6
  9. 9. scientists, engineers and so forth. This is the reason why services offshoringaffects other occupational lines compared to R&D offshoring (see Figure 2 inAppendix 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 thereare offshoring flows similar to trade flows between countries and that these move-ments can be determined by different factors. In this paper, the gravity equationmodel is used to set the relation between R&D offshoring flows in Europe andthe most relevant variables. The gravity model was used in several mannersto assess different flows. However, originally, this model was constructed toanalyse international trade and was applied by some pioneers like Tinbergen(1962), Pöyhöhen (1963), and Linneman (1966). The theoretical basis of themodel came later after many other applications such as in the FDI flows betweencountries (Brainard (1997); Mello Sampayo (2009)). The theory which underliesthe 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 tradecosts i.e. transportation costs which is represented by distance between nations.Using the gravity equation model specifications, this study targets to find whatare the relevant determinants of R&D offshoring flows within European coun-tries. This paper is divided in five other sections where Section 2 clarifies thedifference between offshoring and outsourcing concepts and provides a review ofthe literature about R&D offshoring topic. Section 3 explains the basics and theevolution of the gravity equation model and then provides an empirical back-ground of this model linked to our subject. Section 4 describes the data, thesample used, and the econometric aspect of this study. Afterwards, Section 5brings the results of the estimation. Finally, the paper ends with a conclusion. 7
  10. 10. 2 A broad overview on offshoring2.1 Offshoring vs. outsourcingMany people do not know the clear difference between offshoring and outsourc-ing and, very often, use both terms without any deep comprehension of whatthey are. The following study is based on the definition in Bardhan’s (2006)paper: foreign outsourcing is arms length sourcing to suppliers abroad, andintra-company offshoring is the transfer of production abroad to foreign affili-ates and subsidiaries of European companies, with the objective of exporting theoutput back to the Europe. This definition clarifies the concepts of outsourcingand offshoring in terms of investment decisions.A domestic company may decide to invest to create a foreign affiliate so thatthe latter conducts a certain activity instead of its parent (e.g. manufacturingactivity, IT services, etc.). This action is called by Lewin et al. (2008) captiveoffshoring i.e. the domestic firm keeps the control by owning the majority of theshares of its foreign subsidiary. On the other hand, the national enterprise candecide to offshore certain activities by subcontracting with a foreign partner.This is the offshore outsourcing decision where the foreign partner has the totalcontrol of its supply to the domestic company. The offshoring decision has twomain implications for the concerned company either it decides to offshore and tooutsource its IT services, for instance, or it invests abroad into a subsidiary inorder to offshore and insource its IT services. Hence, we consider two categoriesof offshoring: captive offshoring and offshore outsourcing. The key differencebetween these two concepts is based on the control from the home company onits offshored activities. 8
  11. 11. 2.2 Literature review and offshoring trendsThe typical view on offshoring is always defined by the Northern countries whichoffshore some of their basic activities to the Southern countries in order to ex-ploit a cost advantage in those locations. Antras and Helpman (2004) defineda theoretical model with two countries, the North and the South, for analysingthe global sourcing strategies. They found that “high-productivity firms acquireintermediate inputs in the South whereas low-productivity firms acquire themin the North. Among firms that source their inputs in the same country, thelow-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 integratedfirms. Helpman (1984) introduces a model of vertical foreign direct investmentin order to explain the intra-firm trade related to the intra-firm internationaloutsourcing.According to PRTM, a large management consultancy firm, and World Trademagazine survey the first concern for a large number of companies in the US,Europe and Asia is the offshore transfers and related outsourcing topics. Thesurvey found also that this issue is not the inherent prerogative of the hugeMNEs3 as many small and medium-size structures are intensively prospectingoffshore opportunities. Moreover, we know that since the 1980s outsourcingof manufacturing activities to low-cost countries is usually practised (Dunning(1993); Lee (1986); Vernon (1966)) and even more routinised now. The surveyshows that offshoring decisions are not limited to manufacturing industry butalso apply to a wide range of industries, “[...] from consumer services to hightech.” 3 The acronym MNE refers to “Multinational Enterprise”. 9
  12. 12. Looking at the material outsourcing, a large part of the studies found an in-creasing extent of international outsourcing of material inputs over time (seeFeenstra and Hanson (1996), Campa and Goldberg (1997), Hummels, Ishii andYi (2001), Yeats (2001), Hanson, Mataloni and Slaughter (2004), and Borga andZeile (2004)). Additionally, Egger and Egger (2006) show that, for Europeancountries, there is a negative impact of international material outsourcing onthe 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 (Feenstraand Hanson, (1996, 1999)) and the United Kingdom (Hijzen et al., (2002)) showalso that outsourcing of unskilled labour-intensive parts of production processesfrom relatively skilled-abundant countries to unskilled-abundant countries leadsto an increase in the relative demand for skilled labour in the skilled-abundantcountry and hence increases the skills premium.For at least a decade, there has been a new trend of globalisation which isconcerned 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 largesuppliers of services for developed nations. The increase of this type of trade isdue to more offshoring for this kind of activities and concerns a large range ofservices like “back-office services such as payroll; customer-facing services such ascall-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 telecommunicationsnetworks.” The same authors argued that the largest growth in offshoring ishappening in business services4 . 4 “Business processes is a general term to refer to the collection white-collar processes thatany bureaucratic structure undertakes in servicing its employees, vendors, and customers suchas human resources, accounting, auditing, customer care, telemarketing, tax preparation, etc.”- Rafiq Dossani and Arvind Panagariya (2005). 10
  13. 13. 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 Europeanlevel by using the dataset of the publically available European RestructuringMonitor (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 ( centres). In order to lower labour costs and have access to well-educatedpool of workers, European offshoring is moving principally to Eastern Europe.These authors pointed out that offshoring induces the movement of low-skilljobs out of Western Europe whereas offshoring of innovation-related activitiesand the relatively high-skill jobs remain within Western Europe.The press links offshoring with job losses but Amiti and Wei (2004) show thatthere is no evidence to support this assumption. In fact, a large part of developednations are not specifically more outsourcing-intensive than many developingcountries. More precisely, many developed regions tend to have surplus i.e., therest of the world outsources to them rather than the contrary. The top providersin services are firstly, the United States and secondly, the United Kingdom. Theauthors explained that service outsourcing would not induce a reduction in ag-gregate employment while it has the potential “to make firms/sectors sufficientlymore efficient, leading to enough job creation in the same sectors to offset thelost 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 relativelyundiffused practice. In fact, innovation-related activities are still difficult to off-shore because they imply intangible goods such as the knowledge, the skills, theeducation, etc. Furthermore, the domestic firm may have to support a higherrisk in this kind of offshored activities as its product development depends onthe ability and the availability of highly-skilled people in a too distant foreign 11
  14. 14. location to provide the expected results. Consequently, the distance betweentwo entities depending on each other is important because one needs to sellnew products or new services resulting of an intensive R&D activity to growprofits and another needs the previous one because its production has not anyvalue outside their relationship. The information asymmetry can become a hugeproblem in the relationship between offshored activity and the domestic parentor partner as well.On the other hand, the new wave of offshoring of R&D activities originatesfrom 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 anoffshoring model. This change is due to at least one major reason which is theincreasingly global nature of sales of large firms. Indeed, if a firm expands itsmarket share throughout the world it needs to design its products in line withlocal tastes, leading to the strategy to “design to the market” and even to “designand research to the market” which adds to the previous strategy to “produceto the market”. According to Bardhan and Jaffee, there is a huge potential ofskilled labour in China and India. In consequence, there is an outward transferof 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 strategicdriver (Bunyaratavej et al., 2007; Deloitte, 2004; Farrell et al., 2006; Lewin &Couto, 2007; Lewin & Peeters, 2006) and implies more than just the offshoringof 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 andEngineering (S&E) talent. Based on the annual Offshoring Research Networksurvey results, a large part of US and European companies have started to em- 12
  15. 15. ploy S&E skills in different areas in the world. This trend is due to a shiftof clusters providing highly-skilled people from Western countries to emergingnations such as China and India which have invested more in education andinnovation in order to curb and gradually “reverse” the brain drain.3 The gravity equation modelFor this paper we decided to use the gravity equation framework to assess theR&D offshoring flows because of the wide empirical history and applicationsof this model on bilateral trade flows in the beginning (see Tinbergen (1962)),and on other flows like FDI between nations later on. This model generallyprovides interesting macro-level results about the influences of some factor ontrade-flows. In our case, this is a completely new application of the gravityequation model which estimates the relationship between offshoring flows andsome determinants. The next sub-section provides the basics about the theoret-ical aspects of the gravity model and the other sub-section presents an overviewof the empirical literature.3.1 The classical gravity equation modelIn the standard gravity equation, trade flows between a pair of countries areproportional to their masses (GDPs) and inversely proportional to the distancebetween them. Numerous studies used the basic form of this model and showedrelevant empirical results. This form is expressed as following: Mij = αYiβ Yjγ Niδ Nj dµ Uij 5 ε ij (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 dijis the distance between nations i and j. Usually, we assume that the Uij termis a lognormal distribution error factor with E(ln(Uij )) = 0. Some authors like 5 This equation comes from Anderson’s paper (1979) where he explained the theoreticalfoundations of the gravity equation model. 13
  16. 16. Anderson (1979) defined the theoretical foundations of this model which hadfirstly more empirical specifications.On the other hand, according to Kimura and Lee (2006), it has been foundthat the gravity model can be deducted from different models as Ricardian,Hecksher-Ohlin and the monopolistic competition model. Indeed, Helpman andKrugman (1985) have shown the possibility to derive the gravity equation fromthe monopolistic competition model with increasing returns to scale. MoreoverDeardorff (1998) found that one can derive a gravity model from a Heckscher-Ohlin model without assuming product differentiation. A gravity relationshiphas 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 equationis part of any model of international trade.The gravity equation model was used by Frankel and Romer (1999) to assess theinfluence of trade on growth by using the same bilateral trade data as Frankelet 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 observationswhere registered bilateral trade is zero. Their findings fit other empirical resultsi.e. trade as a fraction of GDP is negatively correlated to distance, is positivelycorrelated to the size and population of the j th country, etc.Despite its successful applications and theoretical basis, the gravity equationfrom an empirical point of view has some limitations and mismatches whenthere is no trade between a pair of countries. Indeed, the majority of empiricalstudies log-transformed the bilateral variables (trade, FDI, etc.) in order to havea consistent log-normal distribution depending on the log-normal distributionof explaining variables. However, this log-transformation eliminates a part ofthe observations on the bilateral dependent variable i.e. for the zero-value. Asa 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
  17. 17. 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 zerotrade from the data set and afterwards estimate the remained log-transformedobservations by OLS. Unfortunately, those methods can produce inconsistentestimators 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 withthe covariates.” These authors propose a solution based on a constant elasticitymodel 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, accordingto their results, the “income elasticities in the traditional gravity equation aresystematically smaller than those obtained with log-linearized OLS regressions.In addition, in both the traditional and Anderson−van Wincoop specificationsof the gravity equation, OLS estimation exaggerates the role of geographicalproximity and colonial ties.” Consequently, the regression analysis of this paperis built on the comparison of different estimation models as PPML in order toget the best and the most relevant estimators. 6 Some raw data for the bilateral dependent variable Tij can be equal to zero, so the solutionto 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 equalto zero, in log-term). 15
  18. 18. 3.2 Empirical backgroundThe present study does not focus on the corporate-level decision to offshoreits key activities but tends to estimate the importance of some factors on theR&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 bilateralcountry flows. Other studies focused on a more disaggregated level about therelationship 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 authorsconcentrated on the imitation and innovation concepts which are importantmodes of technological development. They used a gravity equation model toestimate the potential progress effects of innovation and imitation on a sampleof 20 countries. The results are that the increase in size of the science-basedmanufacturing industries leads to a greater intra-industrial trade between coun-tries which approximates innovation-based technological growth. As usual inthe gravity model, the distance decreases the trade flows. R&D expenditureshave a significant and positive influence on the progress indicator.Regarding the effect of technological innovation on international trade, Ramosand 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 relationshipbetween exportations and creation of technology and between exportations anddiffusion of old technology. However, the relations between exports and diffu-sion of recent innovations and between exports and human skills are definedby an inverted U-shaped chart. To overcome the complexity to capture all theaspects of technologies, they used in their empirical analysis an index calledTechnological Achievement Index7 which is based on four dimensions: creation 7 This composite index was firstly introduced in 2001 by UNDP in its Human DevelopmentReport 2001 - Source: UNDP (2001) Human Development Report 2001, Oxford UniversityPress, New York. 16
  19. 19. of technology, diffusion of recent innovations, diffusion of old innovations andhuman skills.When assessing whether better information can eliminate the effect of geograph-ical distance, Loungani et al. (2002) find some heterogeneity between developedand developing nations. Indeed, within the different determinants of interna-tional trade technological innovation constitutes “a substitute for distance indeveloping countries (better information lowers the effect of distance), whereastechnological 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 significanteffect and they have a greater weight when exchanging differentiated productscompared to exchanging homogeneous products. These empirical results showthat it is important to take into account and to bear in mind the non-linearinfluence 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 ruleof 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 supportrisks linked to the business and corruption might increase it more than otherfactors. According to De Groot et al. (2004), the institutional quality has aclear and positive effect on bilateral trade flows. They used a gravity equationmodel to estimate the influence of institutions on trade. Their model showsthat good governance lowers transaction costs for trade between high-incomecountries, while trade between low-income countries suffer from insecurity andtransaction costs.The regional trade agreements (RTAs) have an influence on trade flows betweencountries. Some authors studied this kind of determinants within Europeancountries. According to Stack (2009), the RTAs effects on trade focus on the 17
  20. 20. enlargement process rather than the deepening of trade integration between EUmembers. She quotes that in a part of the empirical literature the sign andsignificance of trade policy effects can differ. This is due to the existence of biasbecause of omitted relevant variables in the analysis. Stack used a dataset ofbilateral flows from 12 EU countries to 20 OECD trading partners between 1992and 2003. The results show that the positive and significant coefficient estimateof the European trading bloc dummy variable declines in magnitude with anincreasing degree of heterogeneity in the model. According to these results, itis 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 equationmodel. They used a panel of data from a sample of 20 countries (Mercosur withChile and the EU15 bloc of European countries) in order to clarify the timeconstant country-specific effects and also to take into account of relationshipsbetween the relevant variables over time. They found that the fixed effect modelis more relevant compared to the random effect gravity model. They added somevariables to the basic gravity equation and the estimation results show that theinfrastructure, income differences and exchange rates are important explana-tory variables for bilateral trade flows. Specifically, the exporter and importerincomes 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 oftrade flows within Europe. Hansson and Olofsdotter (2008) studied the influ-ence of tax differential on a sample of bilateral FDI flows for the EuropeanUnion members over the period 1986-2004. They found that tax differentialsare important determinants explaining FDI flows. Indeed, the marginal effectivecorporate tax rates between host and investing countries have a negative im-pact on FDI flows. De Mooij and Ederveen (2006) argued that tax differentialsinfluence FDI, but that the magnitude vary substantially and is sensitive to em-pirical specification as well as time periods and countries considered. Because 18
  21. 21. of those shortcomings, the present paper does not include the taxation issue inits estimation of R&D offshoring flows in Europe.4 Data description and econometric aspect4.1 Data sourcesOur bilateral dependent variable data over three periods (2007, 2008 and 2009)comes from the Eurostat database. This dependent variable is part of theBalance 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 astotal 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 Europeanpartners. We assume that the transaction flows between a pair of countriesin R&D services is the sum of payments exporting firms, located in country i,receive from foreign external partners or foreign affiliates/parents, situated incountry j, in delivering offshored (in- or outsourced) R&D services as a resultof the offshoring of innovating-related activities in country i. Our assumptionand proxy variable for R&D offshoring are in line with the statement from VanWelsum and Reif’s (2005) paper that there does not exist direct official datameasuring the extent of offshoring. These authors take trade in total services 8 “The Balance of Payments (BoP) systematically summarizes all economic transactionsbetween the residents and the non-residents of a country or of an economic area during agiven period. The Balance of Payments provides harmonized information on internationaltransactions 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
  22. 22. 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&Dservices 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 byturn the host position and the home position for three periods of time. It isassumed that the ith country, called “host”, welcomes offshored innovation ac-tivities and receives payments from the j th country, called “home”, which paysfor offshored R&D services coming from the host country. We take this specificsample through an inductive process i.e. we selected each country with respectto the availability of the data for our dependent variable over the consideredtime 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 thetotal population (aged 15 to 64 years) i.e. the people who attain at least the firststage of tertiary education9 (higher education, university degree, etc.) and forGDP. The reason to consider the first variable is that highly educated popula-tion constitutes an important factor in offshoring literature (see Bunyaratavej etal., 2007; Deloitte, 2004; Farrell et al.,2006; Lewin and Couto, 2007; Lewin andPeeters, 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 ineach partner is proxied by the share in GDP of gross domestic expenditure inR&D whereas the infrastructure level is based on the level of Internet access inpercentage. Both variables may affect the R&D offshoring flows as a country in- 9 According 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 ofeducation i.e. from the first to the second-level of tertiary education. 20
  23. 23. vesting in innovation and infrastructure is likely to be a favourite location wherecompanies offshore. The data for the latter variables are taken from Eurostatas well.From the GDP data and population data, we calculate the income per capitadisparity variable which is our explanatory variable that capture the effect ofincome differences between partners on offshoring flows. This variable is definedby 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 isricher than the East. This gap is likely to have an impact on the choice of eachpartner to offshore or not.The six Kaufmann indicators measuring the quality of institutions10 are part ofour explanatory variables as well (see Appendix C, Table 3 to have a completedescription of each indicator). In line with Dollar and Kray’s paper, governancemay 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 dimensionof governance. It spreads out from −2.5 to +2.5, the higher the indicator, thebetter 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 3.5to them in order to be able to calculate logarithms.We built a dummy to capture the membership of both countries taken intoconsideration in the Euro Area bloc. The results of Stack’s paper (2009) leadto 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 relevantthan an EU dummy because our complete sample of countries is part of the 10 “The governance indicators aggregate the views on the quality of governance provided by alarge number of enterprise, citizen and expert survey respondents in industrial and developingcountries. These data are gathered from a number of survey institutes, think tanks, non-governmental organizations, and international organizations.” - Kaufmann et al. (2010). 21
  24. 24. European Union whereas the European currency Union dummy evolved overthe chosen time line. Then, the rest of variables were taken from the CEPIIdatabase which provides the distances between capitals and dummy variablesindicating whether two countries are contiguous and share a common officiallanguage. The distance proxies the transportation cost and constitutes a signif-icant determinant of trade flows. The contiguity and a common language arerespectively geographical and cultural aspects whose effects were studied in abroad part of the gravity literature and intuitively may have a positive impacton R&D offshoring. For instance, a company will prefer to offshore a part of itsactivities in a close-by location and/or a country with a similar culture in orderto ease communications and keep control on it.4.2 Statistical discussion4.2.1 Which are the top favourite locations for offshoring?If we look at the last column in Table 5 (see Appendix C), the top 5 providersof 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 seemsthat lots of firms offshored their innovation centres in those locations in order tobenefit from the highly-skilled labour force and the knowledge from this mainWestern European countries. Indeed, these nations compose a large part of thetotal highly educated people (see Table 6 in Appendix C) in our sample covering2007 to 2009. Once exception is Austria which is one of the favourite offshoringlocations but which has not a large highly-skilled labor pool when looking atits share in the total highly-educated population in our sample (2%). In thiscountry, the labour factor might be fully exploited in R&D activities and betterthan in other countries. For instance, although Poland has a 10% proportionin the researchers and engineers population of our sample it possesses a smallparticipation in the R&D offshoring flows. Hence, despite the fact that high-skilljobs remain currently in Western Europe (see Knell and Rojec (2009)), there is 22
  25. 25. an opportunity for this country to become more and more a favourite offshoringplace 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 countrieswhich composed the most favourite locations for offshoring are also more or lessthe top importers of innovation services. More precisely, the 5 most importantimporters are, by descending order: Germany, France, The Netherlands, Italyand 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 isthat Austria is not present. Austria benefits from R&D centres set up withinits borders by foreign companies and becomes one of the largest net providers ofR&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 ofR&D services. These nations are most likely to trade together. Hence, theremay be huge intra-offshoring flows within this region as a Western Europeancompany tends to offshore more in other Western European countries than inother parts of Europe.4.2.3 The importance of educationA highly-educated population refers to people who attain at least the first stageof tertiary education (higher education, university degree, etc.) into the agebracket from 15 to 64 years. Such a population is required in each countryto expand the research in key subjects like biomedicine, biofuels, new businessprocesses, etc. Consequently, we can assume that the evolution of a highly-skillspopulation 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 aredependent from the number of researchers and engineers in a given country. Thisis the reason why some foreign companies from different countries where thereare not enough well-educated people might offshore their innovation activities 23
  26. 26. to a location with a large pool of engineers or people with a PhD diploma, forexample. If we compare Table 5, 6, and 7 (see Appendix C), the top performersin terms of R&D offshoring flows have a huge share in the sample in terms ofgross domestic expenditures. For instance, Poland has an important potentialto become one of the favourite destinations to offshore innovation activities fromforeign companies. This country gathers several advantages like a well-qualifiedpopulation which is correlated with larger gross domestic expenditure in R&Dthan other Eastern European countries. Figure 4 (see Appendix B) shows aclear relation between gross domestic expenditure in R&D and highly educatedpopulation.4.2.4 Offshoring flows between blocsAt a more aggregated level, if we consider some blocs of countries such asWestern European countries (Austria, Germany, Denmark, France and TheNetherlands), Southern European countries (Cyprus, Italy and Greece), andCentral and Eastern European countries (Bulgaria, the Czech Republic, Lithua-nia, Latvia, Poland, Romania and Slovakia) called respectively WEC, SEC, andCEEC, we would have other interesting results in terms of offshoring flows. Theweight 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 topproviders of services in innovation activities. Southern, Eastern, and CentralEurope 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: almosthalf of the volume of services provided by the Western countries is done byGermany. So, Germany is one of the most favourite places to offshore R&Dactivities.Such differences in offshoring flows between these blocs might be explained bya simple hypothesis that comes from the theoretical foundation of internationaltrade. More precisely, this assumption, the main one in the gravity equation 24
  27. 27. 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 interms of GDP (see Figure 5 in Appendix B) provide lots of services in R&Dwhich means that many companies from other parts of Europe offshore theirinnovation activities in these locations. The weight of these top 5 countries inthe offshoring flows and the share of each of them in GDP terms in our sampleare positively correlated. Looking at Figure 5 (see Appendix B), we observethat Germany has the highest weight in size and offshoring flows. Table 2 (seeAppendix C) summarises the results according to the dimensions of size andoffshoring inflows performance. This table classifies the different countries ofour sample and, as we can see, Austria has an interesting position as a smallcountry but with a high performance in R&D offshoring flows. So despite itssmaller size than Germany, Austria has nearly the same weight in the sample interms of offshoring flows. The other top countries have an intermediate positionin R&D performance and have a different ranks depending on their size. Therest of our sample is situated in the bottom-left position on the chart (see Figure5 in Appendix B). However, we can notice that Poland tends to leave this lattergroup.4.3 Econometric specificationTo assess the different determinants of the R&D offshoring flows across Europe,a gravity equation is specified and estimated. The following equation definesthe additive form of the relation between the offshoring flows and these deter-minants: log(Of fijt ) = β0 + β1 .DISTij + β2 .EM Uijt + β3 .CON T IGij +β4 .COM LAN GOF Fij + β5 .log(Eit /GDPit ) +β6 .log(Ejt /GDPjt ) + β7 .log(HRSTit /P OPit ) +β8 .log(HRSTjt /P OPjt ) + β9 .log(W EBit ) +β10 .log(W EBjt ) + β11 .log(DISPijt ) + β12 .log(GDPit ) 25
  28. 28. +β13 .log(GDPjt ) + β14 .log(GOVit ) +β15 .log(GOVjt ) + εijt (2)where log refers to the natural logarithms. Of fijt denotes the outward flowsin 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 islikely that, the more a country i exports R&D services to country j, the morefirms in country j have offshored their innovative activities in country i. TheDISTij variable is the distance between the capital cities of partner countries.EM Uijt is a dummy which denotes if both partners are part of the Economicand Monetary Union depending on the time period11 . CON T IGij variable is adummy as both countries i and j are contiguous whereas COM LAN GOF Fijis 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, thelevel of infrastructure in Europe and the share of Human Resources in Scienceand Technology (HRST) in the total population of each country. The levelof innovation in each country is proxied by Eit /GDPit and Ejt /GDPjt whichare the share of gross domestic expenditure in GDP of each partner. W EBitand W EBjt are based on Internet penetration data (percentage of householdwith Internet access) and approximates the infrastructure level in each partner.The proportion of HRST in the total population is evaluated by the variablesHRSTit /P OPit and HRSTjt /P OPjt that refer, respectively, to the percentageof 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, universitydegree, etc.). 11 Some countries of our sample became members of the Eurozone only in 2009 that is thereason why we have to take into account time for this dummy. 26
  29. 29. The income disparity per capita between country i and j at time t is measuredby the variable DISPijt . GDPit and GDPjt are the gross domestic product ofcountry i and j in current euros and denote the size of each partner. The lastvariables, GOVjt and GOVjt , are based on the six Kaufmann indicators assess-ing the quality of institutions in our sample of countries. The information fromthese indexes was summarised in one variable for each partner via a principalcomponent analysis (PCA). Indeed, in order to eliminate the high correlationbetween the six factors of governance, we transformed these variables in newvariables independently distributed and called principal components. The firstcomponent for, respectively, countries i and j explains mostly the variance ofthe dataset (almost 90%) of the initial variables and so we built one variable foreach partner based on it. Finally, the last term of the equation is the error-termwhich 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 transformedby 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 significancelevel of each estimators for both models. However, following the observationof Santos-Silva and Tenreyro (2006), the Poisson Pseudo-Maximum Likelihood(PPML) estimation method is used because it seems to be the most appropriatemethod to evaluate the gravity equation. Indeed, the log-linearisation providesbad results when observations with heteroscedasticity are present. As well asthe transformed model, PPML estimation takes into account the zero valuesin the dependent variable. Santos-Silva and Tenreyro state also that the OLSestimation of the gravity equation model magnifies the role of “geographicalproximity and links”. Because of these problems, the authors advise to use thePPML estimation method (for further explications, see Appendix A). The next 27
  30. 30. equation is estimated through this method: Of fijt = exp[β0 + β1 .DISTij + β2 .EM Uijt + β3 .CON T IGij +β4 .COM LAN GOF Fij + β5 .log(Eit /GDPit ) +β6 .log(Ejt /GDPjt ) + β7 .log(HRSTit /P OPit ) +β8 .log(HRSTjt /P OPjt ) + β9 .log(W EBit ) +β10 .log(W EBjt ) + β11 .log(DISPijt ) + β12 .log(GDPit ) +β13 .log(GDPjt ) + β14 .log(GOVit ) +β15 .log(GOVjt )].ηijt (3)where ηijt = 1+εijt /exp(xi β) and E[ηijt |xi ] = 1; xi is the matrix of explanatoryvariables. The inference method is based on the Eicker-White robust covariancematrix estimator (see Appendix A).5 Empirical analysis5.1 Determinants of R&D offshoring flowsThe results of the different estimation methods in Table 1 (see page 30) showthat OLS and PPML have a higher explanatory power than the transformedOLS 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 andTeneyro (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 thethree column. If we focus on the third column of the table, the distance whichproxies 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
  31. 31. activities tends to be difficult to offshore because they imply an important riskfor 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 closeto its headquarters to keep control and maintain a good communication withits 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 newservices 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 bothnormal OLS model and PPML model, the EMU dummy coefficient is a rele-vant factor which explains our variable of interest. Indeed, the fact that twopartners are both in the Euro area is positively correlated with the dependentvariable. Two European states which share the same currency will make moretransactions in R&D offshoring terms. It implies that it is necessary for Europeto go forward in the currency union process in order to create a larger and morehomogeneous market and by the way ease transactions between European com-panies.All of the estimation methods are sharing the same view, with the same levelof significancy, on the cultural aspect of each country. Indeed, in Europe, thereare many different cultures, religions and languages in a smaller area comparedto USA where people speak the same language, for instance. In our case, twoEuropean countries with a common official language is positively correlated tothe R&D offshoring flux between them. Hence, being close to each other interms of distance and culture are deterministic factors which tend to influencewhich region a company will choose either to install an offshored subsidiary or tocontract with an external foreign partner to do R&D activities. This is in linewith the fact that we observe intra-flows among Western European countrieswhich share a common history, a connected culture and are close to each other. 29
  32. 32. Table 1: Empirical results OLS OLS PPMLDependent variable log(Offijt ) log(1+Offijt ) OffijtDistance -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 - - 0.87Number of obs. 392 630 630Notes: The numbers within parantheses are the t-statistics. The estimations use Eicker-White’sheteroscedasticity-consistent standard errors. The superscripts (***), (**) and (*) denotesignificance at the 1%, 5% and 10% levels, respectively. 30
  33. 33. 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 investin education to prompt people to speak another language than the national one(English, for instance) in order to facilitate business and transactions betweenforeign 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 positivelycorrelated with the offshoring flows of innovation activities. It seems that thecoefficient in the second column is overestimated in comparison with the twoother estimations. The best estimators are likely to come from the PPMLmethod supported by all the available information. Furthermore, the PPMLestimator of the coefficient of gross domestic expenditure in R&D in the hostcountry is smaller than the one in the home country. But, principally the moreyou do R&D the more you export your expertise in R&D. This is likely to belinked 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 achannel of knowledge and technology diffusion thanks to their foreign Europeanpartners like Germany, Austria (two favourite locations for offshored R&D ac-tivities), etc. Hence, this channel may lead to gain knowledge while increasingthe expenses in R&D in the Eastern region and to invest in delocalised R&Dcentres. The improvement of innovation in Europe is part of the new objectivesof Europe in 2020 with a 3% share of GDP12 on R&D by easing the access toventure capital and by promoting more public spending in R&D. This objectivewill tend to increase positively R&D offshoring flows and, by a snowball effect,will spread innovation throughout Europe. 12 Innovation priorities for Europe - Presentation of J.M. Barrosso, President of the EuropeanCommission, to the European Council, 4th February 2011. 31
  34. 34. In line with the paper of Márquez-Ramos and Martínez-Zarzoso (2010), theInternet diffusion variable denotes how well a country is participating in diffus-ing new technology to acquire knowledge. This factor participates to the levelof innovation in a country. The OLS results in the two first columns appear tobe significant for the level of access to Internet in host country only. The signof the relation is negative which can be explained by the fact that, the more acountry is well-equipped with recent technology, the less it is likely to offshoreits innovation activities because it has sufficient knowledge to do research on itsown. Focusing on the third column, we should be cautious and be aware thatthe latter observation can be biased or overestimated.A last variable which is likely to infer on innovation is the available humanskills in science and technology. This is expressed by the proportion of highlyeducated people in the total population. Intuitevely, the more we have uni-versity graduates the more a country can innovate. The education policy is amajor issue during the 21st century because this can determine the future boomof an economy or maintain a developed economy in the top-rank and even moreso for European countries. The share of highly educated people in a countryhosting offshored innovation activities does not seem to be significant. Thisresult is constrasting with the views of many authors (see Bunyaratavej et al.,2007; Deloitte, 2004; Farrell et al., 2006; Lewin and Couto, 2007; Lewin andPeeters, 2006; Manning et al., 2008) that a large pool of highly skilled workersis a key strategic driver for offshoring in emerging countries such as India andChina. On the other hand, the coefficient estimated for the same variable in thehome country benefiting from foreign partner’s services in R&D appears to behighly relevant. Such a positive relationship can be explained by the fact that awell-educated population in the home country constitutes a required conditionto offshore more towards foreign locations. In fact, a home company which hasoffshored its innovation activities to another European country like Germany,the host country, needs well-qualified people to continue the development afterreceiving the results from the offshored R&D department. 32
  35. 35. 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 thecountries of our sample under two dimensions: their size and their offshoringinflows performance. Table 2 (see Appendix C) provides the summary of thisclassification and large countries such as Germany have a top performance interms 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 otherlarge countries like France. Despite this exception, our results fit the classicalstatement 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 thequality of institutions, the only one which is significantly and negatively corre-lated to the dependent variable is the home country’s governance index. Theimprovement of governance in a country generally permits the increase of tradeexchanges with the rest of the world (see Dollar and Kray (2003)). However, forR&D offshoring flows, such an improvement seems to inhibit a home companyto offshore abroad. Indeed, it will prefer to benefit from the improvement ofthe business environment in its national market and keep all its assets in itsheadquarters.5.2 Robustness5.2.1 TestingIn this section, we conduct some robustness checks in order to test if our modelis well-specified. Firstly, a variance inflation factors (VIF) test is conducted onthe three estimation models in order to measure the multicollinearity. This testprovides 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 theVIF indexes are excessively high (not greater than 10). For a more precise view, 33
  36. 36. Table 11 and 12 (see Appendix C) exhibit the correlation matrix between ex-planatory variables. Despite a few correlation coefficients greater than 0.50, thismatrix confirms the result from the VIF test i.e. a relative low multicollinear-ity among independent variables. Another test called linktest and available inSTATA is used to test specification errors. This test allows to check that, ifthe model is properly specified, one should not be able to find any additionalregressors which are statistically significant unless there is a misspecification ofthe model such as an omitted relevant variable. The output of this test indicatesa misspecification of the PPML estimation method.5.2.2 Checking by bloc of countriesFinally, we would like to check if the determinants of R&D offshoring flows arethe same by comparing with a new estimation by bloc of countries through OLS,transformed-OLS and PPML. These blocs of countries are Western Europeancountries (WEC: Austria, Denmark, France, Germany and The Netherlands),Southern European countries (SEC: Cyprus, Greece and Italy) and Central andEastern 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, exceptfor the CEEC bloc. Looking at the last column of Table 8 and 9 (see AppendixC), 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 aswell as SEC bloc and the rest of Europe. In the case of Southern Europe, thedistance has a large negative relationship with offshoring flows. In constrast,contiguity does not seem to be a positive factor for transfer from West andEast to other nations. It is inconsistent with the previous result and it may bedue to misspecification in the model. Table 9 (see Appendix C) infers that thegross expenditure inside the Southern bloc plays a negative role in the offshoringprocess. Companies will not outsource their R&D department in the South if 34
  37. 37. this region augments its expenditure in research and development. The rest ofEurope prefers to offshore in Southern locations when there is a clear gap interms of innovation. In other words, the South attracts more offshoring flowsby maintaining a difference in innovation level between her and other Europeancountries.Besides, Eastern and Western Europe seems to offshore more when their levelof innovation increase as well. This is in line with general empirical resultsand it supports the fact that there is a reciprocal knowledge transfer betweenWest and East. The coefficient of home’s gross expenditure in R&D variablefor the Southern bloc is not consistent with what we explain in the previousparagraph 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 positiverole. Extending this result, we can assume that a Southern European nationpossessing a well-developed infrastructure (power lines, broadband connexion,etc.) prompts more enterprises to set up their innovation centres within itsborders. Although a country may tend to provide more services in R&D if itis fully equipped, we note a contrast with Eastern Europe. The infrastructureimprovement 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 fromwhich 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 twocountries (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-educatedpopulation in these two blocs, the lower is the offshoring flux. A foreign firmwill prefer to keep control of knowledge in its organisation and not to diffuse 13 Human resources in Science and Technology. 35
  38. 38. it in order to avoid imitation or industrial espionage. As a general empiricalresult, the foreign partner tends to offshore more its R&D department in theSouth of Europe when its population of researchers and engineers rises. Sucha fact is probably linked to the previous i.e. keeping control of knowledge andinformation about developing products along the value chain. For example, acompany may ask its subsidiary or external partner in the South to develop anew product but the final step in designing it occurs at home to prevent fromknowledge spread and/or imitation.Except for Southern nations providing services in R&D, the bigger your arethe more you attract offshoring flows. With respect to income disparity, themost significant results go to the Southern and Eastern European regions. Itis clear that the difference in income level between partners is not positivelycorrelated to offshoring flows in these blocs. Consequently, a foreign firm maydecide to offshore more in a country from these areas if it has a similar level ofincome. The quality of institutions is positively correlated to offshoring flows tothe West and the contrary to the South. By the way, a foreign partner is morelikely to build an affiliate or a relationship with an external partner in the Westof Europe when legal conditions constitutes an advantage. For the Southernbloc, it is the reverse. However, good governance in the home country influ-ences domestic companies to offshore much more in the South. This might bedue to a too restrictive business environment. The partner in the home countrywill offshore its R&D activity to prevent such a situation and to benefit from amore permissive or corrupted state. 36
  39. 39. 6 ConclusionAs 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 firmsand also intangible assets such as knowledge, skills, education, etc. For the samereasons, 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 notthe 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 couldbe easier to find them in a more aggregated view. However, from this point ofview, the tough element is to get an offshoring measure from which we can infersome results. This study focuses on R&D offshoring flows between Europeannations and uses a proxy to measure these flows. A gravity model is built toassess 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 closelocation in order to lower the cost of transportation and even more to keepeasily control on its foreign assets or foreign partners. The fact that two part-ners share the same currency constitutes an advantage which prompts firms tooffshore 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 toinstall an offshored subsidiary or to contract with a foreign arm’s length partnerto do R&D activities. Principally, the more two partners do R&D (increasinggross expenditure in R&D), the more they exports their services in R&D. At thelevel of Western and Eastern European blocs, the gross expenditure in R&D ineach bloc has a similar impact on offshoring which might show the existence of areciprocal knowledge transfer among these parts of Europe. So, R&D offshoringtends to spread innovation throughout Europe and can be a positive factor forfuture growth within the European Union. 37
  40. 40. Another implication from this study is to drive Southern nations to invest morein their infrastructures (power lines, broadband connexions, etc.) in order toattract more companies to set up R&D assets within their national borders.Such a policy will promote diffusion of new technology and increase innovationin 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 countryhosting offshored innovation activities seems not to be significant. Despite thisgeneral result, at an aggregated level, in the WEC and SEC blocs, the higherthe well-educated population, the less they provide services in R&D. Unlesskeeping at home the final step of research and development, a foreign firm willprefer to keep control of knowledge and information within its organisation andnot to diffuse it along the value chain in order to avoid imitation or industrialespionage. 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 theR&D process in its headquarters. The “secret recipe” of a company necessaryto complete the process of development is kept at home whereas the rest andless 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 distanceand culture, the more they trade together, the level of income plays a similarrole. Neighbouring nations such as Austria and Germany will exchange moreservices in R&D thanks to their similarities (level of income, culture, language,etc.) than completely different nations would do. A good quality of institutionsconstitutes an advantage for Western countries. Indeed, a foreign partner ismore likely to build an affiliate or a relationship with an external partner inWestern Europe when legal conditions are favourable to do business.In light of these results, we can recommend some policy implications at theEuropean level to improve the business environment and to promote the intra-European offshoring of innovation-related activities: 38
  41. 41. 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 governanceUnfortunately, such findings do not have the presumption to be the most rele-vant ones about offshoring of R&D services in Europe. Future research shouldexpand such a model more broadly at the international level by collecting dataon offshoring flows between major economies such as Europe, USA, China, In-dia, and BRICs countries. Indeed, the factors explaining these flows are likelyto be somewhat different compared to intra-European factors. The competitionon taxation regimes between countries could really be a relevant factor for studyin the case of R&D offshoring and may imply a new policy at the international 39
  42. 42. 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. Inaddition, our results are based on a proxy which is not only linked to offshoringof R&D services. Consequently, one needs to have a specific accounting itemin the Balance of Payments for transactions by type of products entirely dueto 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 byincluding and testing more other explanatory variables and, hence, to producemore consistent and interesting results. 40
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  48. 48. AppendixAppendix A: Silva and Teneyro’s modelAs suggested by the economic models and Greene (2005), the gravity equationpredicts the expected value of variable of interest, y ≥ 0, for a given valueof the explanatory variable, x. Silva and Teneyro take a constant-elasticitymodel of the form yi = exp(xi β) as suggested by the economic theory and itis 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 anerror-term associated to it. So, let assume that the stochastic model is definedby the following expression: yi = exp(xi β) + εi , (4)with yi ≥ 0 and E[εi |x] = 0. The previous equation can be written as following: yi = exp(xi β)ηi , (5)where ηi = 1 + εi /exp(xi β) and E[ηi |x] = 1. Assuming that yi is positive, themodel can be linearised by taking logs: ln(yi ) = xi β + ln(ηi ), (6)where ln(E[ηi |x]) = 0; E[ln(ηi )|x]) = 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 tothe conditional mean, E[yi |x] = exp(xi β) ∝ V [yi |x], and β can be estimated bysolving the following set of first-order conditions: Σn [yi − exp(xi β)]xi = 0 i=1 (7)As we can see, the estimator defined by equation (7) is numerically equal to thePoisson Pseudo-Maximum Likelihood (PPML) estimator, which is often usedfor count data. However, as the authors said in their paper, the “data do nothave to be Poisson at all - and, what is more important, yi does not even have to 46
  49. 49. be an integer - for the estimator based on the Poisson likelihood function to beconsistent. This is the well-known PML result first noted by Gourieroux, Mon-fort, and Trognon (1984)”. The required condition for the estimator expressedin equation (7) to be consistent is the correct specification of the conditionalmean E[yi |x] = exp(xi β). As explained by Silva and Teneyro, the assumptionthat the conditional variance is proportional to the conditional mean is unlikelyto hold, this estimator does not take full account of the heteroskedasticity inthe model, and consequently all inference has to be based on an Eicker-Whiterobust covariance matrix estimator. 47
  50. 50. Appendix B: Figures Figure 1: Products and occupations: the firm matrix 48
  51. 51. Figure 2: Selected countriesAustria Czech Repuplic Germany Latvia PolandBulgaria Denmark Greece Lithuania RomaniaCyprus France Italy The Netherlands Slovakia 49
  52. 52. Figure 3: Share of each European bloc in the R&D offshoring inflows on averageNote: WEC: Western European countries; SEC: Southern European countries;CEEC: Central and Eastern European countries.Source: Own calculations. 50
  53. 53. Figure 4: Highly educated population and gross domestic expenditure in R&Don average over the period 2007-2009Notes: 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
  54. 54. Figure 5: Relation between the weight in the sample of country’s size andoffshoring inflowsNotes: 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
  55. 55. Appendix C: Tables Table 2: Classification of countries Offshoring 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