Collection of - European...

Collection of i4g Policy Briefs 2013

High Level Economic Expert Group ‘Innovation for Growth – i4g’

Research andInnovation

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LEGAL NOTICE

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��1 See details for mandate, activities, and performance of the "Innovation for Growth –i4g" High Level Economic Expert Group in Annex 1 of this documentation.

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��2 The documentation of i4g R&I Policy Workshop: 'The changing geography of global R&I – policy implications' 21 November 2013 will be published under. http://ec.europa.eu/research/innovation-union/index_en.cfm?pg=expert-groups

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Part 1- Excellence as top 10%

• There are 63 universities in Europe able to reach the top 10% tier in at least one scientific field.

• There are a few global players, as defined by universities which are in top 10% for at least 10 scientific fields.

• On the contrary, there are many niche players (n=42), as defined by universities which are in the top 10% for less than 3 fields.

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• European excellence in science is largely made of a sea of small players, with a few islands of global players.

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Findings

1. The European Research Area (ERA) is characterized by a high degree of diversity in terms of national public research systems expressed in higher education institutions (HEIs) and in public research organisations or public non-university institutions (PROs). 2. Europe has to benefit from the diversity of its institutional research arrangements in order to improve the performance of the research system in view of academic excellence and particular in view of innovation. 3. To improve knowledge exchange and innovation in Europe, monitoring of HEIs and PROs has to be expanded and better indicators for the knowledge transfer and the innovation system has to be developed.

Recommendations

• Support successful institutions in each national system instead of creating similar institutional set-ups all over Europe;

• Strengthen research universities; • Create or maintain only those PROs that fulfil a mission that cannot be accomplish

at a research university; • Set up a European-wide monitoring system for all public research institutions:

HEIs and PROs; • Include PRO's research performance in global university rankings; • Develop better indicators for knowledge transfer and innovation activities for

monitoring and management of the public research institutions.

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Innovation and the public research organization in the ERA: A comparison between Germany, France and the UK

Jutta Allmendinger and Marvin Gamisch

Introduction

This policy brief will take a closer look at public research organization and performance. It addresses the question: What kinds of research institutions or institutional arrangements are needed in Europe to bring out research excellence and foster innovation? The background of this question is that institutional arrangements of public research vary considerably between EU Member States with regard to the importance of the non-university sector. Do these different kinds of organization have specific advantages – and if so, what are they? Does a strong non-university sector lead to a more innovative research system? Has the German model, with its four major non-university research associations (Max Planck, Fraunhofer, Helmholtz and Leibniz institutes), contributed to the high innovative capacity of the German economy?

That this may be the case is suggested by the Innovation Union Scoreboard3,which lists Germany as an ‘Innovation Leader’ and the UK as an ‘Innovation Follower’ although – according to academic excellence in world rankings of universities – the UK has clearly come out on top for at least a couple of decades.

The policy brief is based on the analysis of public data and empirical studies, as well as on a recent I4G workshop examining the research systems of Germany, France and the UK.4 This paper assesses research performance and innovation ability against indicators of publication output, international alignment, citation rate, ERC grants, international rankings and patent output.

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3Cf. http://ec.europa.eu/enterprise/policies/innovation/facts-figures-analysis/innovation-scoreboard/index_en.htm. 4 Innovation for Growth (I4G) Workshop ‘Public research systems and innovation in Europe. Which kind of research institution/ arrangement of institutions do we need?’ held on November 12, 2012, at the WZB Berlin Social Research Center.

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1. Research expenditure and performance in Europe, the US and Asia

Research and Development (R&D) has become a significant factor for the competitiveness of the economies in the OECD. The European Union’s Lisbon strategy underlined this fact by setting a target of 3% GDP to be spent by EU Member States on R&D activities by 2010. The strategy envisaged 2% private R&D investment, destined to help boost innovation immediately as applied research and to serve as the driving force of modern economies. R&D investments for universities, by contrast, are seen as only loosely linked to innovation, because the emphasis there is mainly on fundamental or basic research that may take a long time to bear fruit for society and the economy. But is it truly the case that public non-university research institutions tend more towards applied research and therefore innovation?

The 2011 Innovation Union Competitiveness Report stated that the European public research system consists primarily of higher education institutions (HEIs) and public research organizations (PROs), which form the public non-university research sector. There are nearly 3,000 HEIs in Europe, including 1,360 research-performing HEIs and 170 highly research-intensive universities. There are no exact figures for non-university research organizations in Europe but the report identified at least 150 large PROs at this point. That, however, is a poor estimate for the number of PROs in Europe. Two-thirds of public institutional funds go to HEIs, whereas one-third goes to PROs. In the European-wide competition for research grants in the 6th Framework Programme, which was strongly geared to technology research, PROs in total performed only slightly less well than HEIs. They received a share of 30.6% of the budget, compared to 37.6% for HEIs.5Relative to their size and number, however, PROs performed much better than HEIs, a fact that supports the assumption that they may be more associated with innovation than HEIs.

In international comparisons of research performance, it is common to rely on input indicators, such as financial investment (e.g. the 3% target), and to measure output indicators such as the number of patents, publications, citations, awards or scores in international rankings. As only the public research landscape is considered, other indicators of innovation, such as high-tech exports or BERD (Business Expenditure on Research and Development), are excluded here.

With regard do input indicators, the EU-27 achieved a 2% R&D share of the GDP in 2010, an average notably below the 3% target. Germany ��

5 Cf. European Commission (2011), pp. 157-161.

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achieved 2.82%, France 2.26% and the UK 1.77%. Only Finland, Sweden and Denmark managed to exceed the target, whereas the ‘big’ member states are far from reaching it. In comparison, the US share was 2.79%, and China’s share was 1.47% in 2008. Nonetheless, the 3% target does not seem so ambitious if one takes into account the shares of Japan (3.45%, 2008) and Korea (3.36%, 2008), innovative economies exporting mostly high-tech products.6

With regard to research output, a Fraunhofer ISI study offers a sophisticated international comparison. Drawing on the Web of Science (WoS), a multidisciplinary database of high-quality journals, it compares bibliometric analyses to indicate the R&D performance of selected countries, including the US, Germany, France, the UK, the EU average and others.

Over time, we observe considerable shifts in performance rates by country. The study reveals a moderate decrease in the relative performance of both the US and the EU. Between 2000 and 2009, the US share in all publications registered in the WoS declined from 32.1% to 28%; the EU-27 share went down from 37.2% to 35.6% Within the EU, Germany, France and the UK all witnessed a reduction of their world share. Germany’s share declined from 8.1% to 7.3% in the same period, Britain’s from 9.3% to 7.8%, and France’s from 5.9% to 5.3%. The Netherlands and Italy, by contrast, could slightly improve their performance. Changing the focus to Asia, we observe a decrease of the Japanese share from 8.7% to 6.3%, whereas Korea could improve its performance from 1.6% to 3.1%. The most striking thing, though, is the tremendous rise of China from 3.5% to 10.3%.7 Clearly, the European countries have to strengthen their efforts to keep up.

There are hardly any indicators to measure the performance of institutions, however. The 25 indicators of the Innovation Union Scoreboard (IUS) are tailor-made for countries, not for single institutions8.

To approach the question of whether institutional designs matter, we consider three different systems. Germany, France and the UK will be compared with regard to their public research institutions. Both Germany and France are examples of a system with a highly developed non-university

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6 Cf. EUROSTAT (2012), p. 32, and EFI (2012), pp. 20f. 7 Cf. Schmoch et. al. (2011), p. 5. 8 The IUS is differentiated into twenty-five indicators grouped into eight innovation dimensions and three main classes of indicators. The high levels of aggregations - the 8 innovation dimensions - already allow for a detailed picture and analysis of country group comparison. See: European Commission (2012).

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research sector, whereas the British system is dominated by research universities. 2. The role of the public research systems of Germany, France and the UK for R&D performance and innovation with special emphasis on the non-university sector

2.1 Scope and structure of the public non-university research sector in Germany, France and the UK

Let us begin by looking at the role of the public non-university research sector in Germany, France and the UK. This look should enable us to identify the advantages and disadvantages of the different organizational research settings and their implications for innovation.

The size of the non-university sector varies between the three countries. In 2010, the GDP share of non-university public research was 0.41% in Germany, 0.37% in France and 0.17% in the UK. In other words, Germany and France are clearly above the EU-27 average of 0.26%, whereas the UK is far below it.9

Thanks to its highly developed non-university sector, Germany possesses an internationally unique research landscape. In 2010, a total of about €10 billion was spent on R&D in the German non-university sector. The expenditure adds up to 44.6% of total spending in the public research sector.10 But the uniqueness is not necessarily in the scope but in the diversity of the non-university research sector, which currently encompasses 1,006 institutions. Some of them are clustered in the big four research associations: the Max Planck Society, the Fraunhofer Society, the Helmholtz Association and the Leibniz Association. In addition, there are academies, ministerial research organizations, libraries, archives, museums and other institutions.11

In 2010, the French non-university sector had an expenditure volume of €6.4 billion, which amounts to 43.5% of total public research spending.12

Regarding its institutional composition, the French non-university sector is shaped by big organizational units like the National Centre for Scientific Research (CNRS) with over 25,000 employees, the Centre for Nuclear Power (CEA) with over 16,000 employees or the National Institute for Agricultural Research (INRA) with around 10,000 employees. Also notable are the 33 Carnot Institutes, which are responsible for the cooperation with ��

9Cf. OECD (2012), pp. 75f. 10 Cf. ibid., pp. 69 and 75f 11 Cf. Federal Statistical Office Germany (2012), pp. 12-13. 12 Cf. OECD (2012), pp. 69 and 75f

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enterprises and the transfer of technology. Some belong to other non-university organizations like the CNRS, whereas others are fully independent. In addition, France has ministerial research organizations.13

The R&D expenditure volume of the British non-university sector amounted to around €2.9 billion in 2010, representing only 26.1% of total R&D spending in the public research sector.14 The non-university sector consists of about 140 so-called ‘Public Sector Research Establishments’ and is dominated by ministerial research institutions mainly dedicated to research in defence and health, which take up over half of the total public non-university R&D expenditure. The institutes of the Research Councils account for 30%; cultural institutions take up 4%. The degree of diversification is significantly lower than that of Germany and France. It is notable that R&D expenditures only constitute a small part of the total expenditure. In the case of the ministerial research institutions, it is only up to 17%.15

2.2 Research and innovation performance of Germany, France and the UK

To assess the research and innovation performance of the different systems, we consider the following indicators: number of publications, university rankings, international alignment, citation rate, ERC grants and patent output.

Number of publications

The 2011 World Report of SCImago provides information about the publication output of single HEIs and PROs. The report looks at 3,042 institutions worldwide that have published at least 100 scientific documents of any kind during 2009, as collected by the scientific database Scopus by Elsevier. The listed institutions were responsible for more than 80% of the global scientific output during 2005-09.16 For the comparative purpose of this paper, we will consider only the German, French and British institutions included in the report. Health institutions are excluded from our considerations, as they cannot be clearly classified as belonging either to the university or the non-university sector. Private institutions are also excluded, because they are not part of the public research sector. ��

13 Cf. Polt et. al. (2010), p. 53. 14 Cf. OECD (2012), pp. 69 and 75f. 15Cf. Polt et. al. (2010), p. 53. 16 Cf. SCImago (2011).

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When using the SCImago rankings, we have to keep in mind that it ranks the Max Planck Society and the Fraunhofer Society as individual institutions representing the research output of 84 Max Planck institutes and 85 Fraunhofer institutes. Institutes belonging to the Helmholtz Association and the Leibniz Association, by contrast, are listed as single institutions. Therefore only a few Helmholtz and Leibniz institutes are visible on a global scale, whereas Max Planck is able to occupy a top position. Unless the statistical database is changed we have to put up with this bias. Moreover, the report does not differentiate between governmental and other non-university research organizations, instead applying the label governmental to all of these institutions. Again, it would be helpful if the data were adjusted.

Comparing the three countries, we can observe important differences. Overall, the ranking lists 112 German institutions (excluding health and private research institutions). The total number of publications is 591,375. With nearly 50,000 publications, the Max Planck Society has the biggest individual share (8.3%), followed by Ludwig Maximilian University of Munich (3.5%) and the University of Heidelberg (3.4%). When looking at the whole picture, however, higher education institutions clearly outnumber the non-university sector, with cumulative shares of about 78.8% and 21.2%, respectively.

In contrast to Germany, France has a non-university research sector that is much more concentrated, and we find a more balanced distribution between both sectors. The ranking features 110 French HEIs and PROs, which, together, have a publishing record of 426,355. The CNRS is the most important player. As a single gigantic research institution, its share amounts to 130,977, or 30.7%, whereas the share of the other two big institutions, CEA and INRA, adds up to 7.2%. By comparison, the most productive universities in terms of publication output are Paris 6 (4.9%), Paris South (3.2%) and Paris 7 (2.9%). The university-non-university sector ratio is 53.5% to 46.5%.

In the case of Britain, SCImago lists107 institutions producing a total of 593,184 scientific publications. What is striking is the overwhelming dominance of higher education institutions, which together have a share of 95.4%, compared to a meagre 4.6 % for the non-university sector. The five leading universities (University College London, Cambridge, Oxford, Imperial College London and Manchester) together have a share of over one quarter (25.9%). According to these numbers, the British public non-university sector seems to be quite insignificant for the publication output.

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University rankings

In international rankings such as the Shanghai and THES rankings, a university’s reputation is predominately defined in terms of its research performance. Excellent research, in turn, attracts excellent researchers, increasing the potential for further innovation. When using this measure, the UK enjoys a clear advantage. Only British universities are able to achieve Top 10 positions in both rankings: two in the 2012 Shanghai ranking, and three in the 2012-13 THES ranking. Expanding our view to the Top 50, we see five and seven British universities in the Shanghai and THES rankings, respectively, whereas only two French universities make it into the Shanghai Top 50, and only one German university into THES. To find both German and French universities, we have to look at the Top 100 of both rankings: Shanghai lists four German and three French universities; THES features four German and four French universities, compared to nine and ten British universities, respectively17 It would be misleading, however, to take the Shanghai or THES rankings as indicators of research quality as such, because countries with a strong non-university sector are systematically misrepresented.

International alignment and citation rate

The category ‘international alignment’ shows whether a country’s researchers publish their work in internationally visible or less visible journals, judged against the world average (0). The comparison between the three countries reveals an advantage for the UK, which in 2007 achieved a value of 25, compared to 21 for Germany and 17 for France. The US value is 34.18

The citation rate considers the actual year of publication and the two following years. Self-citations are excluded. It gives an idea of the quality of the published research. In 2007, the citation rates were 5.1 (UK), 4.8 (Germany) and 4.5 (France), compared to 5.5 for the US.19

ERC grants

ERC grants serve as an indicator of highest academic excellence, because they are assumed to stimulate high-risk research and indicate the ��

17 Cf. Shanghai Ranking Consultancy (2012)/ TSL Education (2012). 18 Cf. Schmoch et. al. (2011), p. 5. 19 Cf. ibid., p. 7.

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attractiveness of a country for excellent researchers. Between 2007 and 2010, British institutions received about 350 grants, compared to about 240 for France and about 230 for Germany. By including information on researchers’ nationality, the grants also illustrate a country’s attractiveness for excellent foreign researchers. The UK attracted the largest number of grantees without UK nationality (far over 150), whereas France hosted over 50 excellent foreign researchers. Both countries have a positive balance in terms of hosting non-national grantees and sending their own nationals abroad. Germany, by contrast, has the most negative balance with over 100 German grantees working outside of their home country.20

Patent output

Comparing the three countries’ patent output (filed under PCT) as a standard indicator of innovation reveals an advantage for Germany, with an absolute number of 17,095 in 2010, compared to 7,111 for France and 5,243 for the UK.21 That does not mean, however, that the German PROs are above-average contributors to the patent output. To get an idea of their share, we can draw on data collected in 2008, when the German patent output (filed under PCT) stood at about 16,990.22 The big four non-university organizations (Max Planck, Helmholtz, Leibniz, Fraunhofer) together produced 1,036 patents (excluding double entries) filed under PCT, the EPO and the German Patent and Trade Mark Office in 2008.23 These figures suggest moderate gains rather than above-average advantages of a highly diversified public non-university research sector.

In sum, we have shown that the UK is top in terms of number of publications, citations, and international visibility. It clearly outperforms Germany and France with regard to both ERC grants and the Shanghai and THES rankings. Although some of the differences can be explained by misleading statistics, we don’t see any systematic advantages of the German or French systems with their large non-university research sectors. It is only in terms of patent output that Germany takes a clear lead, also relative to its population. However, this lead can neither solely nor even primarily be attributed to Germany’s PROs. More plausibly, it is connected with the huge German industrial sector.

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20 Cf. European Commission (2011), pp. 195-197. 21 Cf. OECD (2011). 22 Cf. ibid. 23 Cf. Fraunhofer ISI et. al. (2012), pp. 48-55.

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3. The case of non-university research institutions in Germany24

Leaving these quantitative indicators behind, we now take a closer look at the specific German system, as it is marked by a unique degree of diversification. The four big non-university research associations – Max Planck, Fraunhofer, Helmholtz and Leibniz – are its most prominent feature.What justifies the existence of such organizations? Are there any benefits we cannot grasp by considering ‘conventional’ indicators?

The idea behind the four research associations presupposes specific missions that cannot be accomplished by universities.

The origin of non-university research in Germany lies in the Kaiser Wilhelm Society, founded in 1911 to enable private financing for research and to satisfy the needs of the German industry. After the Second World War, the Max Planck Society, as the successor of the Kaiser Wilhelm Society, focused on interdisciplinary, long-term basic research. In 2010, it employed 12,672 R&D staff (in FTE) and had a budget of about €1.5 billion.25 The federal government and the Länder each contribute 50% of the funding. Max Planck research covers a wide range of fields, with an emphasis on natural sciences as well as on social sciences and the humanities. Researchers are free to determine their own research agenda as long as they meet strictly defined scientific criteria. The unique feature of Max Planck is a person-centered research approach called the Harnack principle, which includes a sophisticated selection procedure and the establishment of an institute around an distinguished researcher, who enjoys a great deal of freedom in terms of how to use the granted resources.

The Helmholtz Association unites several formerly independent centres dedicated to big science. With 25,885 R&D-FTEs and a budget of €3.2 billion, it was the biggest non-university research organization in 2010.26 In general, 90% of the funding come from the federal government, and10% from the institution’s host Land. The funding parties are represented in proportion to their share in the Committee of Financing Partners, which determines research policy requirements and identifies research fields. Consequently, the federal government has an important influence on Helmholtz research. The historical origin of the Helmholtz Association lies especially in nuclear research after the Second World War. The original mission of a highly concentrated and hierarchically organized research organization has changed and was replaced in 2001 by a programme-centred approach involving various smaller research fields. Today, the primary ��

24 The ministerial research organizations are not considered here. 25 Cf. Federal Statistical Office Germany (2010), p. 18. 26 Cf. ibid.

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research areas are medicine, natural sciences and engineering. Accordingly, the actual mission has become obsolete for approximately half of the centres, as they do not engage in big science in the traditional sense.

The Fraunhofer Society offers applied research upon the request of private enterprises or public institutions. Its focus is product-centred research aimed at practical innovation and marketability. It is therefore an important contact for private economic interests. In 2010, Fraunhofer employed 13,926 R&D staff (FTEs) and had a budget of €1.6 billion.27 Around 30% of its budget is financed by the federal and Länder governments, which contribute 90% and 10% respectively. The other 70% are earned through contract research. As Fraunhofer strongly relies on private funds and mandates of clients, the research conducted is client-oriented. As a consequence, the Fraunhofer Society itself is organised like a corporation, with a central management and quality management based on client surveys. The by far most important field of research is engineering, which reflects Fraunhofer’s industry-friendly orientation.

The Leibniz Association consists of research institutes, museums, libraries and service institutions. Compared to the other research organizations presented above, it lacks a clearly defined profile. This has to do with its historical origins: Leibniz was not founded as an organization with a specific mission, but as an umbrella organization for already existing, very diverse institutions whose main similarity was that they were funded by the federal and Länder governments (typically 50:50). In 2010, Leibniz employed 11,270 R&D FTEs, and its budget added up to €1.2 billion.28

How can we evaluate a system like Germany’s, with such big non-university research organizations? What special advantages do they offer?

The Max Planck Society is focused on basic research. In contrast to the professorial chairs at universities, a director of a Max Planck institute is not confined by disciplinary boundaries. Moreover, in 2006-08, the Max Planck Society achieved the highest publication intensity (1.56 per researcher) among the other three organizations and the universities (SCI-Index; universities: 1.32).29 Nonetheless, a system dominated by research universities such as the UK’s is also able to attract excellent researchers and to achieve an excellent research output, especially if measured in terms of ERC grants, university rankings and the SCImago ranking. There seems to be no compelling argument that the specific mission embodied by Max Planck necessarily has to be fulfilled outside of the universities. ��

27 Cf. Federal Statistical Office Germany (2010), p. 18. 28 Cf. ibid. 29 Cf. EFI (2012), p. 45.

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As stated before, almost half of the institutions of the Helmholtz Association do not conduct research in the original sense of big science using heavy equipment. The former nuclear research facilities in Jülich and Karlsruhe, for example, now are committed to smaller research fields like transport, communications and health. On the level of the association as a whole, a clear mission cannot be found anymore. It is a research organization, however, that allows the federal government a high degree of influence on the research agenda.

As indicated above, the Leibniz Association offers no internationally exemplary model, as it never had a specific mission as a whole. Recently, however, many institutes have been looking for opportunities to collaborate more closely with the universities. This juncture may be a model for achieving higher visibility in the years to come.

The Fraunhofer Society offers a sharp contrast to the research university model. Whereas the latter is shaped by a high degree of autonomy, with researchers determining their own research agenda and responding independently to the needs of society, Fraunhofer is dedicated to fulfilling concrete client orders. The research carried out, therefore, is in the specific interest of corporations, and Fraunhofer participates directly in the transfer of knowledge and technology towards the economy. Accordingly, it embodies a specific research mission that is clearly distinct from the idea of independent university research. In Fraunhofer, corporations have a visible business-friendly research partner for needs that cannot be addressed within the university structure or within the corporations themselves. Therefore, it stimulates innovations that can be transferred directly to the market. Moreover, the Fraunhofer Society is far ahead of the other three associations and the universities in terms of declared patents, with 71 patents per one thousand researchers in 2006-08 (universities: 39; Max Planck: 17; Helmholtz: 31, Leibniz: 23).30

In sum, the highly developed and diversified non-university research sector in Germany cannot serve as an easily transferable model. Its origin lies in special historical circumstances, and it is shaped by political federalism. Consequently, the creation of this sector is strongly connected to the constitutional restrictions that keep the federal government from funding the universities directly, a fact that strengthened the development of a non-university research sector, independently of the notion of research missions.31 Moreover, the idea of mission-based research associations that

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30 Cf. EFI (2012), p.45 31 Cf. EFI (2011), pp. 26-41.

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fulfil tasks that cannot be addressed adequately within universities has its limits in the German case. The Leibniz Association does not have a clear identifiable mission as a whole. The Helmholtz Association has abandoned its original task of conducting big science and substituted it by a programme-based approach. Here, the federal government has the best possibilities to exert a major influence on research contents. That, however, makes it difficult to draw a clear line between Helmholtz research and ministerial research. The goal of the Max Planck Society to offer long-term, interdisciplinary basic research led by outstanding researchers with a high academic reputation does not necessarily imply a non-university setting, since research universities are fully able to perform that way, as in case of the British system. The Fraunhofer Society, by contrast, offers a mission that should not be merged with the concept of a research university. Fraunhofer offers research commissioned by clients, mostly private enterprises and public organizations. It therefore represents an important link in the innovation chain, but has grown to become that link since the late 1960s.

3. Conclusion

The analysis of national public research systems of Germany, France and the UK has revealed important differences. All three systems differ with regard to their institutional composition. Whereas the UK has a rather small public non-university research sector, Germany and France have much bigger ones. But the German system seems to be more diversified compared to the French, which is shaped by big single institutions. It has become clear that important specific historical and institutional factors exist that shape the various national public research systems. These factors are hard to overcome, especially in a multi-level governance system such as the EU or the ERA. The ERA, therefore, is characterized by a high degree of diversity in terms of national public research systems.

To foster innovation, it is important to review and develop indicators of public research performance. The existing indicators hint mainly at the commercialization aspects of innovation and fail to recognize the numerous aspects of knowledge exchange with society and the economy as a whole. In particular, the role of universities as drivers of innovation in the service economy has hardly been studied.32

In light of these findings, our conclusion has to concentrate on two issues: First, how can Europe benefit from the diversity of its institutional research ��

32 The work of Alan Hughes could serve as a starting point for such studies, even though it is only focused on UK universities. Cf. Hughes/ Kitson (2012).

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arrangements in order to improve the performance of the research system? Second, how can the European Commission’s portfolio of research and innovation indicators be expanded and/or specified to represent a more holistic or comprehensive conception of knowledge exchange and innovation?

Regarding the performance of the research system, we can conclude that the UK performs much better than other European countries in terms of academic excellence, as expressed in international university rankings, most importantly the Shanghai and THES rankings. Nonetheless, Germany is classified as an innovation leader, as measured by the composite indicator of the European Commission's Innovation Union Scoreboard, whereas the UK, like France, is classified as an innovation follower. The UK might perform very well with regard to the international reputation of some of its universities, above all Oxford and Cambridge; however, it lags behind – especially when compared to Germany – in terms of PCT patent applications, community trademarks and community designs.33

For the abovementioned reasons, it does not seem appropriate for the UK to restructure its excellent universities, nor would it make sense for Germany to abolish the Fraunhofer Society, which performs very well in terms of PCT patents. Before simply copying structures, necessary background conditions have to be met. The success of Fraunhofer, for example, is closely connected to the existence of a huge industrial sector, which is not the case in the UK. Models for research institutions, therefore, cannot be seen in isolation from their context.

In the long run, institutional learning might occur between different national systems in the ERA if a pertinent monitoring system were to be established, based on regular reporting and featuring comparable indicators and intensive information exchange activities. If the ERA and the Innovation Union are to improve their performance, it is advisable to strengthen the successful institutions of different systems instead of creating a similar institutional set-up all over Europe. A step towards identifying institutional features and institutional performance is currently underway with U-Multirank and the Global Research Benchmarking System (GBRS). GRBS offers an in-depth analysis on the research performance of Higher Education Institutions that helps identify niche strengths not reflected in the common university rankings.34 U-Multirank is a monitoring tool designed for conducting an analysis of institutional performance and political strengths and weaknesses. The basic logic is achieving transparency of horizontal diversity. In this context, world-class research is conceived as just one profile among others

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33 Cf. European Commission (2012), pp. 29, 35 a. 51. 34 Cf. http://www.researchbenchmarking.org/web/guest/home.

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relevant to innovation and society. The tool contains six dimensions: teaching and learning, research involvement, knowledge exchange, international orientation, regional engagement and student profile. In contrast to the common international university rankings, U-Multirank does not provide a composite indicator and a general ranking of such diverse institutions.35 Both tools are, however, restricted to higher education institutions. It would be beneficial to have similar tools available for assessing PROs with regard to academic excellence and different dimensions of innovation.

Regarding adequate performance indicators for innovation, it should be emphasized that knowledge exchange activities among academics do not primarily aim at product commercialization. Academic activities besides university teaching include giving guest lectures, attending conferences or participating in networks. Moreover, providing informal advice or consulting services is far more common than the commercialization of products. In a minority of cases, businesses seek the support of universities (at least in the UK) for product innovation, but they do it much more often with regard to marketing and sales.36 Innovation indicators should also reflect this dimension of innovation.

4. Recommendations

Support successful institutions in national research systems.

ERA is regarded as a European public research system consisting of different national systems that have evolved historically and encompass a wide range of universities and non-university research institutions. ERA aims at improving the performance of the research system in terms of academic excellence and of innovation. Therefore, we recommend strengthening the successful institutions in each national system instead of creating similar institutional set-ups all over Europe.

Strengthen research universities.

The UK research system performs best with regard to international visibility, publication output, citation rates and ERC grants. This successful model is based on outstanding, research-intensive universities and should be strengthened. If appropriate, it can provide essential inspiration for other HEIs ��

35 Cf. van Vugh/ Ziegele (2011). 36 Cf. Hughes/ Kitson (2012), pp. 726f.

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within the ERA. It has to be emphasized that for several reasons research universities are the centrepieces of any research system, as they combine research with the education of future scholars.

Create or maintain only those PROs that fulfil a mission that cannot be accomplished at a research university.

PROs should not weaken the capabilities of universities. Therefore they should possess a distinct mission, which cannot be accomplished at a research university.

This is the case when research organizations have to conduct research directly for the needs of the government, other public institutions or private enterprises. In contrast to a research university, the PRO research agenda should be determined largely by its sponsors or clients and correspond directly to their concrete needs. Moreover, there has to be a compelling reason why this mission has to be accomplished within a public institution. The Fraunhofer Society, for example, offers an application-oriented R&D infrastructure based on contracts. Thus it offers customized scientific support to small and medium-sized enterprises as well as other public/private institutions that are not able to conduct R&D themselves. Consequently, Fraunhofer represents an innovation-friendly model, because of its focus on engineering, particularly for product innovation, which cannot be achieved by a research university.

Enhance cooperation between universities and non-university institutions where appropriate.

Even though each institution should have a distinct mission, the channels between universities and PROs should not be closed. The cooperation between universities and non-university institutions can lead to synergy effects if research interests are combined and geographical proximity is given.

Set up a European-wide monitoring system for all single public research institutions.

To achieve better comparability, we recommend establishing a common monitoring system for public European research institutions, including a complete list of European universities and non-university organizations. It would be most important for such a European monitoring system to avoid the bias represented in existing rankings, which tend to distort the situation by showing a preference for large, established institutions and for science and

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medicine, by not considering niche strengths of institutions, and by using only a single source of bibliometric data.

Use the newly developed tools of GBRS and U-Multirank.

The Global Benchmarking Research System (GBRS) provides flexible analytical tools, which permit the user to focus on specific aspects of a university’s research performance and therefore allows for identifying niche strengths. U-Multirank offers a multidimensional assessment of higher education institutions. Six categories are covered, each made up of several indicators. The categories are: education profile, student profile, research involvement, knowledge transfer, international orientation and regional engagement. It is not designed as a ranking, but allows users to rank universities according to their individual criteria, thus making U-Multirank a highly flexible tool.

Integrate non-university public research institutions in the monitoring as well.

Most of the existing monitoring tools, including GBRS and U-Multirank, do not cover public non-university research institutions. As shown above for the cases of Germany and France, the non-university sector can play an important role in a research system. Thus the integration of PROs should also be a primary concern when designing a European-wide monitoring system. As a first step, it would be necessary to identify all of the PROs in the EU/ERA, because the 150 institutions identified so far only represent a small share of the total. When including PROs in the system, some performance indicators (e.g. in U-Multirank) would have to be adapted whereas others inappropriate for PROs (e.g. student profile) would have to be excluded.

Develop indicators of knowledge exchange for public research institutions that also reflect the importance of low-threshold exchange and marketing activities.

Conventional indicators ignore some very important features of the knowledge exchange between public research institutions and private or public partners. A survey in the UK revealed some underestimated aspects in this context. Firstly, the channels for knowledge exchange are broad. Informal contacts, guest lectures or the attendance of conferences are more common than licensing and patenting. Scholars mention a lack of time and university bureaucracy as main obstacles for knowledge exchange activities. Consequently, an indicator should be developed that not only measures the time devoted to research but also the time devoted to low-threshold knowledge exchange activities, for example through conferences with business representatives or civil society actors.

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Secondly, especially in the case of the social sciences, marketing activities and human resource management are main fields of interaction between businesses and universities. An indicator should be developed that reflects these activities and gives the social sciences a more realistic representation in the monitoring process.

Adapt the Shanghai, THES and SCImago rankings or associate PRO research performance with that of the universities.

Although the Shanghai and THES rankings are biased for the abovementioned reasons, they nonetheless remain internationally influential, especially in terms of research reputation. We therefore recommend adapting these rankings in a way to make them display the real research performance also of countries with an important non-university research sector, such as France or Germany. If that is not possible, adapt the national systems in a way that non-university research performance can in some way be associated with that of the universities and thus show up in the rankings.

At this point, the existing tools for directly comparing university and non-university institutions are either missing or very biased. In the case of the SCImago ranking, it would be a necessary improvement to organize non-university umbrella organizations in a way that they can appear as a whole in the report. A new European-wide monitoring tool should follow this suggestion as well.

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education/doc/multirank_en.pdf, checked on 3/04/2013.

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• Business R&D intensity depending largely on the sector composition of economic activity, an increase in R&D intensity in each existing sector cannot compensate for deindustrialisation. �

• Public funding of private R&D may increase the R&D intensity of existing sectors but cannot substantially boost national R&D intensity without an evolution of the overall sector distribution of value added. �

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• The generation of radical innovation and the development of new emerging high tech sectors �depend on both proximity to science and efficient technology transfer processes in specific scientific clusters of fields. �

• Public policies should not only focus on technology transfer, but also promote excellence in emerging scientific fields including the promotion of scientific specialisation.��

• Innovation systems with a sufficient number of excellent universities and where universities are part of excellent research networks will produce more high potential start-ups�

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• Policies of high wage countries have to promote the expansion of knowledge and research intensive sectors, especially emerging sectors, through the development of innovative start-ups.

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Promoting young innovative companies in Europe: Do not forget science!

Innovation is high on policy agendas at both Member states and EU levels. As a result, a number of new schemes are being implemented to speed up technology transfer of academic research, fund business R&D and promote the growth of start-ups. In this context, policy makers should still keep in mind the different determinants of innovation, in particular radical innovation.

During the 2000s, policy makers focused much of their efforts on increasing R&D intensity as part of the Lisbon strategy. Economists have pointed to the fact that the national R&D intensity has strong a structural component and may be considered as an output of the innovation process. As a result, efforts are turning towards the dynamics of R&D intensity (part 1). This paper focuses on the role of start-ups in emerging and fast growing sectors in reducing the transatlantic R&D (and innovation gap). Policy makers do care much these days about start up creation and growth, but they may not always pay sufficient attention to some of the conditions for their rapid development (part 2). In order to explore this issue, the paper discusses the hypothesis of a European paradox, which influenced policy making from the mid-1990s on. As policy makers have been impressed by the so-called European paradox, they have neglected the impact of scientific excellence on the potential for technology transfer and innovation. Part 3 reviews existing evidence and presents bibliometric data to discuss the specialisation of European countries in disciplines related to high potential technologies.

The last part of the paper explores policy implications and relationships with other i4g work.

The dynamics of the Knowledge based economy

Business R&D has long been recognised as an input to the innovation process. At the firm level, conducting R&D is causing product innovation 2 to 4 years later (Raymond et al; 2013). Across industries, business R&D intensity is correlated to proximity to science and new knowledge creation fuelling technological opportunities (Klevorick et al. 1995). At the country level, business R&D intensity is correlated to innovation performance. Figure 1 gives a simple illustration of this macroeconomic correlation based on data from the Innovation Union Scoreboard37.

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37 Let’s remark that the US would be close to Germany if it were to be added on the graph (but there are less indicators available for the US).

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The fundamental role of R&D in the innovation process does not mean that a country’s R&D intensity, or even its business R&D intensity, is a direct policy variable. This is why the 3% target decided as part of the Lisbon Strategy has been questioned.

Indeed, at the macroeconomic level, R&D intensity may also be considered as an output of the innovation process (van Pottelsberghe 2008). Since R&D intensity depends on fundamental sector characteristics, each sector exhibits a set range of R&D intensity. In other words, in each country, R&D intensity depends on the sector distribution of value added.

Figure 1 Business R&D and innovation performance

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Figure 2 shows the difference between R&D intensity as it is usually observed and R&D intensity adjusted for industry structure. It shows that the industrial structure of Korea, Finland and Germany is very favourable to their high business R&D intensity38. The industrial structure of the Netherlands and France is on the contrary unfavourable. Interestingly, it is also the case, to some extent, of the United States despite its quite high R&D intensity. In the latter case, it can be related to the high share of services in value added. At the same time, some service sectors exhibit high R&D intensity in the US and, overall, services represent more than 28% of business R&D, against 7% in Germany (OECD 2011).

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47

Figure 2. Business R&D intensity adjusted for industry structure, 2008

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Source: OECD Science, Technology and Industry Scoreboard 2011

Sector composition thus plays a fundamental role in the R&D gap between the EU and the US. Figure 3 clearly shows that high tech sectors represent a much higher share of business R&D in the US. The share of high tech sectors has increased on both sides of the Atlantic during the 2000s, but the gap has remained: the EU remains specialised in mid-high tech sectors while the US has further increased its specialisation in high tech sectors. More generally, an analysis at the firm-level analysis confirms that across-sector differences dominate over within-sector differences (Stančík and Biagi 2012).

The importance of the industrial structure to explain R&D intensity has led to consider the dynamics of structural change as a major issue for the development of the knowledge based economy and potential growth. In this perspective, increasing R&D intensity is not a target, but rather an indicator on which changes in the industrial structural have a major impact.

Policy makers should then turn their efforts towards the speed of industrial structure change in Europe. In recent economic history the US has experienced larger and quicker changes in its industrial structure than a number of European countries. The US have in particular generated new product and service sectors based on new technologies.

48

Figure 3. The distribution of R&D spending by R&D intensity in the EU and US,

2002 and 2011

Source : European Commission JRC-IPTS

Analyses dealing with these issues have underscored the role played by young high tech or knowledge intensive firms in this dynamics. Some large companies manage to keep productivity and innovation up in their sectors, as illustrated by German companies in the car or machine tool industries. However, rapid growth in new sectors typically depends on radical innovation brought to the market by new companies.

These dynamics are confirmed by the analysis of the age structure of R&D leaders as they are registered in the Industrial R&D Scoreboard (EU 2011). Table 1 shows that the share of yollies in R&D is more important in the US across the board, but especially in the most recent activities such as biotechnology, internet and software. These activities correspond to sectors of technological specialisation for the United States as measured by the relative technological advantage (Veugelers and Cincera 2010, OECD 2012).

49

Table 1. R&D intensity and proportion of yollies by sector and region, %

Source : Veugelers and Cincera (2010)

Besides, since the 2000s open innovation have developed and large incumbents draw on both academic teams and start-ups companies to speed up their innovation process and get access to radical new ideas and technologies.

As a result, since the late 2000s, policy makers have been reflecting on the way to promote the growth of start-ups in high tech and knowledge intensive sectors (Guellec and Sachwald 2008, Veugelers 2011).

In a systemic perspective on innovation, several policy areas are relevant, including financing and business environment in particular. This paper focuses on one aspect which seems insufficiently explored: the potential pool of scientific results on which radical innovation may draw.

50

Quality of the scientific base, start-up creation and innovation

Advances in scientific understanding represent a major source of technological opportunities for firms. Based on a detailed survey conducted in the 1980s, Klevorick et al. (1995) showed that these advances have two components. First new advances increase the pool of knowledge available to solve problems. This can explain the importance of applied disciplines for a large set of industries, including the automobile or defence industries. Second, in some cases, direct advances generate new opportunities, such as the emergence of biotechnology and its deep impact on the creation of new firms and a new set of actors in the pharmaceutical industry. In these less frequent cases, firms organise to be connected to academic research and not only rely on hiring well trained engineers or use disembodied knowledge through .publications for example.

At a macroeconomic level, this corresponds to the general observation that radical innovation tends to draw on frontier research. In turn frontier research may be characterised along at least two dimensions: excellence and contributing to a new and dynamic scientific area.

This section considers more specifically the impact of scientific excellence on the creation of start ups and new establishments of existing companies in the neighbourhood of universities. Next section provides indicators of frontier research by geographical zones, taking into account both the quality and specialization dimensions of academic publications.

Firms have increased their links to academic research over the last 25 years, which can be related to a combination of the availability of new scientific results and more systematic efforts to put such results to commercial use through innovation39. Indicators of these closer links include a higher number of collaborative agreements between firms and research institutions as well as the creation of start ups from academic research. Both firms and scientific institutions have organised to take advantage of increased opportunities to innovate on the basis of academic research results. Since the late 1990s, this trend has been enhanced by the more general adoption of open innovation models by companies.

Empirical studies have shown that various links and cooperative agreements with academic research generate more technology and significant patents (Cassiman et al. 2007). Regarding firm entry, the location of star scientist has been an important factor in biotechnology during the 1990s. Zucker and Darby (2003) have shown that the location of scientists publishing breakthrough articles is correlated with firm entry in nanotechnology. These empirical results may be related to the fact that technology being transferred from academic research is typically at an early stage and needs to be further developed in connexion with the researcher herself. In other words, technology transfer requires an interaction between codified knowledge (e.g. patents) and un-codified knowledge.

More recent contributions have further explored the impact of proximity to a university on the probability of start-up creation.

A study of the Japanese cluster policy, has specificaly estimated the impact of University-Industry Partnership (UIP) on innovation (Nishimura and Okamuro 2010). The policy has not provided direct support, but incentives and services to further develop research and innovation networks within existing clusters selected by METI. The policy aimed more specifically at stimulating public-private research partnerships between SMEs and “national” universities, which are the excellent universities

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39 As suggested by some analyses of the impact of the Bayh Dole Act in the USA. In the case of biotechnology, see for example Mc Millan et al. (2000).

51

in the Japanese system40. Participation to a selected cluster (Industrial Cluster Partnership, ICP) aimed at allowing SMEs to choose the most appropriate partners within the cluster, within the region or neighbouring regions. The scheme provides for R&D funding, support to set up networks, to develop start-ups, to spend on marketing and training. From 2001 to 2005, 19 clusters have benefitted from the scheme.

The study has analysed data for 229 SMEs that has engaged in a partnership with academic research between 2002 and 2004. Among those, 57 were involved in an ICP. R&D productivity has been measured through patent data: number of patents, number of claims, number of citations. Control for the quality of patents aimed at checking that an increase in the number of patents was not due to pressure from METI to patent results firms would not have spontaneously protected in order to generate more favourable policy indicators. The analysis shows that simple participation to an ICP does not increase R&D impact on patents. This can be explained by the fact that about a third of ICP participants have never actually used the specific services provided by the programme.

Second, collaborations with academic research within one region or with the neighbouring region tend to reduce the number and quality of patents out of R&D spending. On the contrary, collaboration with more distant academic institutions tend to increase the number of patents without diminishing their quality. Finally, when local public-private collaborations through an ICP involve a « national » university, there is a positive impact on the number of patents. In other words, local collaborations only stimulate innovation when they involve an excellent academic partner. These Japanese results confirm the importance of the choice of a relevant partner, even if it is located far away. Long distance relationships may be justified by relevance and the search for excellent partners (Miotti and Sachwald 2003, D’Este and Iammarino 2010).

A recent study using data on new knowledge intensive firms (KIFs) in Italy investigates more directly the role of university knowledge with respect to start up creation (Bonaccorsi et al. forthcoming). It shows that knowledge codified in academic patents from one Italian province positively affects new KIFs creation in other provinces, having a spatial range of 200 km. Knowledge codified in publications and embedded in university graduates is much more localized: their effect on new KIFs creation is confined within the boundaries of the province in which universities are located. Besides, the spatial range of university knowledge is shaped by the quality of the universities producing this knowledge. Here again, the quality of university knowledge appears to increase its economic impact.

An American study further shows that university patents have a positive impact on the probability of firm creation in the neighbourhood (Hausman 2012). This impact is measured by establishing a correspondence between the patented technologies and the sectors in which new companies are created. The study is able to measure a positive impact of higher federal funding (from NIH and DOD).on the creation of new companies around beneficiaries. Overall, university patents have a positive impact on local employment (75 miles around the university) and a stronger impact close to the university. In this study, the new establishments may be subsidiaries of existing companies. As a result university attractiveness results in more companies, with a mix of start-ups and new establishments of existing companies. This is the case in particular in the pharmaceutical industry, where new R&D centres have been attracted to excellent academic centres. These establishments may represent numerous jobs, but start-ups tend to experience higher growth.

These empirical results suggest that the quality of knowledge creation is an important determinant of start-up creation around universities. This confirms the more general positive impact of research

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excellence on technology transfer as measured by licensing. Conti and Gaule (2011) have for example showed that the licensing gap between the US and Europe is partly explained by the quality of academic results.

The quality of research in university is especially relevant since academic researchers represent a small share of start-up creators, even in high tech or fast growing emerging sectors. Indeed, students and graduates are much more numerous and represent a much larger potential pool of entrepreneurs (Astebro et al. 2012).

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The quality and specialisation of the EU scientific base

The so called “European paradox” stated that Europe was conducting frontier research and good at it, while it was less able than the US to innovate from its research results. Since the late 2000s this hypothesis European paradox has been examined more closely with bibliometric data and the European paradox has been revisited. The issue can be analysed along the two dimensions of frontier research.

Firstly, excellence indicators rank European publications behind those of the US (figure 4). Europe has nevertheless a higher share of the most cited publications than of the total publications, which is the reverse for Asia. From this type of excellence indicators Europe is thus behind but relatively close to the US and ahead of Asia. These are overall results and do not deal with relative productivity in publications where Europe was shown to lag behind the US (Dosi, Llerena et Sylos-Labini 2005).

Figure 4. Share of world scientific publications, 2009, in %

Source : calculation from WOS

As for the second dimension of frontier research, scientific specialisation of large European countries and of the EU in general is not favourable to the most dynamic sectors of innovation. Andrea Bonaccorsi (2009) has underscored EU’s weak scientific performance in comparison with the US in information technology. He has also suggested that EU is relatively weak in the new fast developing scientific fields such as nanotechnology and life sciences.

53

Figures 5 summarises the position of Europe, the US and Asia for the most cited publications and a number of disciplines. It clearly shows the overall strong positions of the US and Europe, but the parity of Asia on a few disciplines: in ICT, material science, one area in physics and pharmacology. Europe leads in astronomy, geosciences and specialties in physics, agronomy and engineering. The US leads in biological, biotech and medical disciplines, chemical sciences as well as in the multidisciplinary sciences to simultaneously show three bibliometric indicators: the share of world publications, the impact for the 10% most cited and specialisation by disciplines.

Figure 6 focuses on ICT related scientific disciplines such as computer science, artificial intelligence and telecommunication. It shows that both the EU and the US are not specialised while some Asian countries on the contrary are strongly specialised in some of these disciplines. However, Asian publications have generally low impact. Overall the three large regions have a similar share of world publications (between 19% and 25% depending on disciplines), while Europe and the US exhibit a similar impact indicator. As a result the US produce the largest national pool of high potential scientific results to innovate in ICT.

Figure 7 focuses on biotechnology and biomedical engineering. Europe has only a slightly lower share of world publications than the US, but it is less specialised and the impact indicator is lower. Asia in these disciplines is in a much weaker position. Within Europe, national positions are quite diverse. Small specialised countries such as Denmark are not included.

54

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Conclusions and policy implications

Based on available data and empirical analyses this paper has made three points:

1. Business R&D intensity depending largely on the sector composition of economic activity, an increase in R&D intensity in each existing sector cannot compensate for deindustrialisation;

2. The generation of radical innovation and the development of new emerging high tech sectors depend on both proximity to science and efficient technology transfer processes;

3. There is a significant transatlantic “excellence gap” in the scientific disciplines with most innovation potential.

These results have a number of policy implications.

First, public funding of private R&D, whatever the policy instrument, may increase the R&D intensity of existing sectors but cannot substantially boost national R&D intensity without an evolution of the overall sector distribution of value added. In the case of France for example, data suggests that the R&D tax credit has had a positive impact on sector R&D intensity, resulting in only a small positive impact on the macroeconomic business R&D intensity as it compensated for the lower share of manufacturing in value added. As a consequence, in high wage countries, policies have to also promote the expansion of knowledge and research intensive sectors, especially emerging sectors, through the development of innovative start-ups. The successful development of these activities will simultaneously contribute to growth and push R&D intensity up.

Second, high potential start-ups often thrive on radical innovation. In turn radical innovation depends on access to excellent scientific results in a number of fields. As a consequence, in order to promote growth in knowledge intensive sectors, public policies should not only focus on technology transfer, but also promote excellence in emerging scientific fields. This has implications both for the promotion of excellence and for scientific specialisation.

Third, students and young graduates representing a large pool of potential entrepreneurs, they should be exposed to excellent research. This suggests that innovation systems where there is a sufficient number of excellent universities and where universities are included in the best research networks will produce more high potential start-ups.

These results and policy implications dovetail with other contributions from i4g (current and last year):

- cluster policies and scientific excellence, or clusters vs. networks; - measurement of scientific performance of European universities; - promotion of start-ups/yollies

Finally, these policy implications would have to be considered at both the national and EU levels. A priori, the three H2020 pillars address the relevant issues, but it would be useful to work on the articulation with national policies and to explore more precisely the issue of excellence in relation with specialisation in disciplines or areas of high innovation potential.

58

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Bonaccorsi, A., M.G. Colombo, M. Guerini, C. Rossi Lamastra, forthcoming,The spatial range of university knowledge and the creation of knowledge intensive firms, Small Business Economics,

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Appendix Data

Data from the WOS data base have been extracted by OST. Humanities and social sciences are not included.

Europe includes 86% of total EU publications: Germany, France, Austria, UK, Netherlands, Italy

Asia includes: China, Korea, Taiwan and Japan

Definition of indicators:

i, discipline; j,country ; w, world

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62

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Findings

• Peripheral areas do not have the same capacity to foster local innovative interactions as large cities.

• Large urban agglomerations are able to build up large innovative capacities and supportive institution while remote and/or peripheral regions frequently lack the critical mass to keeping constant the levels of innovation.

• Therefore, question marks emerge about the viability and capacity of clusters in relatively small and isolated locations. Clusters may lead to lock-in situations.

Recommendation

• Viable alternatives for remote and/or peripheral regions are the promotion of ‘pipelines’ or international connections and long-distance knowledge exchanges as a means to introduce new knowledge and to enable a better absorption and diffusion of innovations generated outside their economic area.

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• Various approaches of inclusive innovations (In-In) originally practiced by developing countries (frugal innovation, jugaad innovation in India) are assessed on their impact on and relevance for advanced economies.

• Inclusive innovation introduces a paradigm change – More performance is produced with Less resources for Many people (MLM).

• New business models of western companies for the large pro-poor market in developing countries should be envisaged.

• As inclusive innovation addresses social economy aspects in the developing world as well as in poorer regions of Europe or for poorer population groups policies should open up to new approaches and respond quicker to In-In initiatives.

• Market failures particular in the health sector (malaria, aids, etc.) for poorer population segments may be compensated by public research subsidies, public prizes or public procurement.

• Experimentation and piloting regarding the support for inclusive innovation to mitigating unemployment problems, in particular of the young, highly educated people is suggested.

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References

Dutz, M.A. (2007), Unleashing India’s innovation: Toward sustainable and inclusive growth.Washington D.C., World Bank.

Foster, C. and R. Heeks (2013), “Conceptualizing inclusive innovation: modifying systems of innovation frameworks to understand diffusion of new technology to low-income consumers”, European Journal of Development Research, forthcoming.

Kaplinsky, R. (2011a), “Schumacher meets Schumpeter: Appropriate technology below the radar”, Research Policy, 40(2), 193-203.

Kaplinsky, R. (2011b), “’Bottom of the pyramid innovation’ and pro-poor growth”, IKD working paper No. 62.

77

Kaplinsky, R. (2013), “Innovation for pro-poor growth: From redistribution with growth to redistribution through growth”, in A. Cornia and F Stewart, Essays in Honour of Sir Richard Joly,forthcoming.

NESTA (2012), Our frugal future: Lessons from India’s innovation system. NESTA, July 2012. http://www.nesta.org.uk/library/documents/OurFrugFuture.pdf

OECD (2011), Divided we Stand. Paris: OECD.

OECD (2013), Innovation and inclusive development, Discussion report revised February 2013. Paris: OECD. http://www.oecd.org/sti/inno/oecd-inclusive-innovation.pdf

Prahalad, C.K. (2005), The fortune at the bottom of the pyramid: Eradicating poverty through profits.Upper Saddle River, NJ: Pearson/Wharton School Publishing.

Prahalad, C.K. and R.A. Mashelkar (2010), Innovation’s Holy Grail. Harvard Business Review July 2010.

Universe Foundation (2013), What are frugal solutions? http://www.frugalsolutions.org/What/What-are-frugal-solutions.aspx

von Hippel, Eric (1986), "Lead users: a source of novel product concepts", Management Science 32(7): 791–805.

Whitesides, George (2011), The frugal way: the promise of cost-conscious science. Economist, 17th

November, 2011.

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49 This report does not necessarily reflect the views of the European Commission. The findings are based on a number of previous presentations and discussions of the i4g High Level Economic Expert Group of DG RTD.50 Research excellence of universities is a count of articles in international quality journals that combine volume, impact and quality: volume of research (number of articles in Scopus refereed journals); impact of research (4-year h-index, derived from the number of citations received); quality of research (% of articles published in the top 25% and 10% journals)

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51 I4G, Policy Brief n°11 'Innovation and the public research organization in the ERA', J. Allmendiger, M. Garmisch, 2013. 52 'Are European universities facing the Asian Challenge in excellent S&T research?', A. Bonaccorsi et al., I4G, (2013) 53 I4G, Policy Brief n°11 'Innovation and the public research organization in the ERA', J. Allmendiger, M. Garmisch, 2013.

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European excellence in science (being part of top10% performer) is an archipelago of small players, with only a few islands of global players.

The first 7 US global players (Harvard, John Hopkins, MIT, UCSF, Pittsburgh, Stanford, Ann Arbor) account for a larger number of scientific fields they dominate in than all European universities combined.

The list of Asian global players is not dominated by countries with a tradition in science, such as Japan and Australia, but by Singapore, China, South Korea and Hong Kong.

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1. Innovation is one important driver of long-term growth, but it may also widen the social divide in the society, form a group of innovation losers in the population and lead to more exclusion. As a consequence one could consider to enforce innovation in a direction of addressing a broader part of the population and/or to establish a new approach for innovation geared to social inclusion.

2. The policy concept suggested in the paper is focused on a new and somehow complementary approach to innovation. It complements the concept of excellence – the main concept for innovation in the EU – and the concept of regional innovation. The additional third leg of innovation addresses societal goals like low cost for high tech products, cheap services based on modern ICT infrastructure, sustainability, equal opportunities in dis-advantageous regions.

3. An economic concept comparable to the one for ‘social enterprises’ or for ‘gemeinnützige Wirtschaft’ (non-profit economy, or more verbally the economy serving the common interest and the public good) could help to create an economy with a significant sector of entrepreneurial entities aiming at inclusive innovation. The stylized indicators of such a sector are (i) not for profit (and if at all not more than 4% ROE) and (ii) no priority for dividend payment and rather re-investment for existing or future inclusive innovation projects.

4. This rather broad concept could include approaches like ‘frugal innovation’.

5. Specific programs financed by the budget of the EU could be crucial to create first and stabilize later a sector achieving innovation in combination with inclusiveness.

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2. Complex contractual environment for cross-border-leasing with up to 28 big folders in a foreign language relying on more than one foreign jurisdiction requires sophisticated customers in the local municipal parliament for approval.

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Introduction The new EU Member States from Central and Eastern Europe still compete internationally mainly on the basis of low input costs, but the sources of their existing competitive advantage, in particular low labour costs, are gradually disappearing. Their competitiveness, when expressed as labour costs in relation to the average of the EU15 countries, has been gradually deteriorating. How can the new EU member states in Central and Eastern Europe build and strengthen their innovation-based competitive advantage in order to compete with China and other Asian countries? This question becomes important because the emerging Asian economies still maintain the Ricardo- and Heckscher-Ohlin-type of comparative advantage in trade based on low-cost inputs, and at the same time have been progressing with technological development (Tan, 2011; Heiduk, McCaleb, 2012).

This policy brief discusses conditions for an optimal policy to strengthen the innovativeness and international competitiveness of new European Union member states in Central and Eastern Europe (EU1155). First, changes in comparative advantages in the foreign trade of EU11 countries are analyzed, including shifts towards gaining advantages in trade of technologically advanced goods since their EU entry. Second, the limitations to the restructuring of the EU11 innovation systems that hamper a move toward innovation-driven competitive advantages in foreign trade are identified. Third, recommendation regarding science, technology and innovation (STI) policy, which could support changes in these countries’ national innovation systems enabling them to effectively compete on international markets with emerging economies in Asia, are formulated.

How do the EU11 countries compete with Asia? The EU11 countries, still compete internationally mainly on the basis of low input costs, but the sources of existing competitive advantage of the EU11, in particular low labour costs, are gradually disappearing. Hungary, the Czech Republic and Slovakia had the smallest advantages in the segment of labor-intensive goods56, while Bulgaria and Romania had the strongest (albeit shrinking) advantages. In this context, it is worth highlighting the position of China, which still has strong advantages in the trade of labor-intensive goods—its revealed comparative advantages (RCA) indices are substantially higher than the corresponding indices for EU11 countries.

Only two of the studied EU11 countries—the Czech Republic and Slovakia—had comparative advantages in the trade of goods produced by mainstream manufacturing (MM) industries throughout the five-year period of 2005 to 2010. These advantages were relatively ��

55 The EU11 are the former socialist countries that joined the EU in 2004 (Poland, the Czech Republic, Slovakia, Hungary, Slovenia, Estonia, Latvia, and Lithuania), 2007 (Bulgaria and Romania), and 2013 (Croatia). This last country is included in the analysis only when statistical data was available for it. 56 Manufacturing industries have been divided into five groups based on how different types of inputs contribute to production costs (Peneder, 1999): 1) mainstream manufacturing, in which tangible factors (labor and capital) are as important in the manufacturing process as intangible factors (research and marketing), with labor prevailing over capital among the tangible factors; 2) labor-intensive industries, in which labor plays a predominant role in the production of goods, while marketing inputs tend to be more intensive than other intangible factors; 3) capital-intensive industries, in which investment in physical capital plays a dominant role; 4) marketing-driven industries, with relatively high spending on advertising and brand building accompanied by a relatively low intensity of investment in capital; 5) technology-driven industries, with high expenditure on R&D.

94

stable over time. The remaining EU11 countries did not have advantages in the trade of goods produced by mainstream manufacturing industries The structure of comparative advantages in the trade of capital-intensive goods has been far more diverse. Three EU11 countries, Romania, Bulgaria and Slovakia, had comparative advantages in trade of such products, but these advantages decreased steadily between 2005 and 2010. Poland, the Czech Republic and Hungary improved their positions steadily, but by 2010 they did not achieve competitiveness in this area. It is worth noting in this context that China had no comparative advantage in the trade of capital-intensive goods, and, after a short-lived improvement during the global crisis of 2008-2009, in 2010 this country lost advantages in trade. Since their accession to the EU, some of the EU11 countries (Poland and Bulgaria, for instance) have developed comparative advantages in marketing-based industries. However, China has the strongest advantages in this area.These advantages emerged and grew rapidly after 2005 in a trend that can be attributed to the activities of foreign investors and their impact on China’s foreign trade (Tan, 2011; Heiduk, McCaleb, 2012). When it comes to products from technology-driven industries, the structure of comparative advantages in the EU11 countries changed the most substantially in 2005-2010. Hungary stands out positively in the trade of these goods while Slovakia also gained and increased its advantages in the period of 2005-2010. Other EU11 countries were not competitive in the trade in this group of goods, yet their competitiveness increased gradually. This compares unfavorably with China’s track record in this area. This emerging Asian economy gained comparative advantages in the trade of technology-intensive goods in the five-year period from 2005 to 2010.

Progress in Innovation System Restructuring in EU11 Countries: Problems and Challenges

As “an effective innovation system should have an effect on international competitiveness and on the trade balance of more sophisticated products and services” (EC, 2013a p. 4), it is worth identifying the limitations to the restructuring of EU11 innovation systems that hamper a move toward innovation-driven competition and toward competitive advantages in the trade of technologically advanced goods. �Despite their similar historical experiences and a shared track record of decades under central planning followed by a transition to a market economy in the 1990s, new EU member States from Central and Eastern Europe are not a homogeneous group. This is evidenced by significant differences in the competitive position of these economies in international league tables (WEF, 2012; IMD, 2013) and by a varying degree of convergence with the EU15. In six EU11 economies, namely, Poland, the Czech Republic, Slovakia, Lithuania, Bulgaria and Romania, the convergence process accelerated after these countries’ EU entry in 2004, while in Estonia, Latvia, Slovenia and Hungary, the GDP per capita gap to the EU15 narrowed at a slightly slower rate than before the accession (Matkowski, Próchniak, 2013). Overall, in 2004-2012, all EU countries from Central and Eastern Europe except Slovenia narrowed the gap to the EU average in GDP per capita in PPS terms. Slovakia and Lithuania improved their positions by 18 percentage points, Poland, Romania and Latvia each improved by 15 p.p., Bulgaria gained 12 p.p., and Estonia 11 p.p. The smallest changes in GDP per capita relative

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to the EU average in 2004-2012 were recorded in the Czech Republic (by 1 p.p.) and Hungary (by 3 p.p.), while in Croatia the level of GDP per capita in relation to the EU average increased by 5 p.p. in 2004-2012. Slovenia was the only analyzed country whose GDP per capita decreased with regard to the EU27 average between 2004 and 2012—by 5 p.p. However, EU11 countries restructured their innovation systems with some delay compared to the speed of GDP convergence in this region. The convergence of GDP per capita in all EU11 countries, except Slovenia, was accompanied by relatively slower changes in these countries’ innovativeness measured with the Summary Innovation Index (SII). The SII in most EU11 countries grew at a slower rate than the EU27 average in 2004-2011. The only exceptions are Slovakia and Slovenia, whose SII indices progressively converged with the EU27 average. However, in Slovenia, this was accompanied by a divergence of real GDP per capita against the EU27 average. There are several reasons behind the relatively slow growth rate of SII in the EU11 countries. Low R&D expenditures with a dominant role played by the government sector is one the problems. Overall, the EU11 innovation system underwent some restructuring in terms of R&D financing from 2004 to 2011, yet in most countries this was limited to an increased role for foreign financing (including EU funds). There, the business sector is still not the main source of research funding. However, in four countries, i.e., Estonia, Hungary, Lithuania and Slovenia, the opposite trend was noted: the role of the business sector in R&D expenditure increased; though only in Slovenia and the Czech Republic did it exceed 50% and approach the EU average. Human capital is a strong point of innovation systems in most EU11 countries, and human capital indicators in most of these countries grew at a faster rate than the EU average. In 2012, the percentage of the population aged 25-64 with more than a secondary education in the EU11 ranged from 75.9% in Romania to 93.4% in Lithuania and was higher than the EU27 average of 74.2%. Similar trends were displayed with regard to the percentage of employees with a university degree in mathematics and computer science in the total working population. In Poland, the Czech Republic and Romania, this indicator was above the EU27 average. In most EU11 countries (except Romania, Latvia and Slovenia), it increased between 2004 and 2012, while the EU average dropped slightly. The performance of EU11 countries in terms of Excellence in S&T57 varies substantially in terms of the index and its growth rate. Hungary is the best in the region, but still worse than the EU average. On the other hand, Hungary’s Excellence in S&T index is nearly three times as high as Latvia’s. Estonia, in turn, stands out in terms of the rate at which the index grew; it grew almost three times as fast as the EU average. By contrast, Latvia’s Excellence in S&T index decreased between 2005 and 2010.

Similar picture can be seen when the participation of EU11 countries in the 7th Framework Program (FP7) is analyzed. On average, the participation of EU11 countries in (FP7) is far below their potential, and there is room to improve their position with regard to EU15 member states (Rauch, Sommer-Ulrich, 2012). Moreover, in most EU11 member states, ��

57 It is a composite indicator of four variables: 1) the share of highly cited publications in all publications where at least one of the authors has an affiliation in a given country; 2) number of top scientific universities and public research organizations in a country divided by million population; 3) PCT patent applications per million population; 4) total value of ERC grants received divided by public R&D performed by the higher education and government sectors. For detailed methodology see: EC, (2013a), Research and Innovation Performance in EU Member States and Associated Countries. Innovation Union Progress at Country Level, Directorate-General for Research and Innovation, European Commission , p. 321.

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working conditions in FP7 projects are not attractive enough for highly productive researchers (EC, 2013a). Additionally, all across the EU11, the so-called Index of Economic Impact of Innovation58

was lower than the EU average of 0.61 in the 2010-2011 period. However, the figures vary considerably from one country to the next. The countries that stand out positively in the EU11 group in terms of the Index of Economic Impact of Innovation are Hungary (0.53), Slovenia (0.52), the Czech Republic (0.50), Slovakia (0.48), and Estonia (0.45). The varied level of progress in restructuring EU11 innovation systems can further be seen from analyzing the Knowledge Intensity of the Economy Index.59 At the end of 2010, Hungary was the only EU11 country where the index exceeded the EU27 average. Estonia and Slovenia were slowly approaching the EU27 average, while the remaining EU11 countries still have a long way to go before they catch up with the EU as a whole. There is also a diversity regarding the EU1 technological profiles. Some countries (Bulgaria, Poland, Slovakia, and Slovenia) have advantages in advanced technology such as information and communication (ICT), others (the Czech Republic, Estonia, Hungary, Latvia, Poland, and Slovenia) in environmental technology, and still others (Romania, Bulgaria, and Latvia) in nanotechnology. Some countries specialize in various traditional industries as well, such as agriculture and food processing (Bulgaria, Croatia, Poland, Slovakia, and Slovenia). This means there is no uniform model of technological expertise in the EU11, which has important implications for innovation policy. To wrap up this discussion of national innovation system restructuring in the EU11, it seems appropriate to revisit the questions about innovation as the basis for the international competitiveness of EU11 economies. It turns out that, in most EU11 countries, national innovation system restructuring has been insufficient to meet competition from emerging Asian economies, particularly China, but these countries are steadily enhancing their competitive advantages in high- and medium-high-tech goods. This conclusion is confirmed by the indices that represent a contribution of high- and medium-high-tech manufacturing to the trade balance.60 Most EU11 countries had a structural surplus in the trade of high- and medium-high-tech goods in 2011. The highest indices were recorded in Slovenia, Hungary and Slovakia. Between 2000 and 2011, the index grew the fastest in Croatia, the Czech Republic, Poland and Slovakia. Only four EU11 countries, Latvia, Bulgaria, Estonia and Lithuania, ran up a structural deficit in the trade of high- and medium-high-tech goods. ��

58 The index is composed of five indicators defined in the Innovation Union Scoreboard 2013: 1) PCT patent applications per billion GDP (in PPS€); 2) employment in knowledge-intensive manufacturing and service as % of total employment; 3) contribution of medium- and high-tech exports to trade balance; 4) sales of new-to-market and new-to-firm innovations as % of turnover 5) knowledge-intensive service exports as % of total service exports. For detailed methodology see: EC, (2013a), p. 320. 59 This is a composite indicator of structural change, which measures changes in the actual sectoral composition of the economy. It is composed of eight indicators organized into five dimensions: 1) the R&D dimension; 2) the skills dimension measures; 3) the sectoral specialization dimension; 4) the international specialization dimension; 5) the internationalization dimension refers to the changing international competitiveness of a country in terms of attracting and diffusing foreign direct investment (inward and outward foreign direct investments). The methodology is summarized in EC, (2013a), pp. 321-322. 60 The index shows the difference between the observed industry trade balance and the theoretical trade balance. A positive value for an industry indicates a structural surplus and a negative value means a structural deficit. The methodology is explained in: OECD (2007) and EC(2013a), p. 322.

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On the basis of the discussion conducted above, it can be concluded that EU11 innovation systems have not been sufficiently restructured to effectively compete with emerging Asian economies in the technology-driven industries. The only exception is Hungary, which leads the way in most indicators describing national innovation system restructuring in the EU11. Hungary also has comparative advantages in the trade of goods produced by technology-driven industries (TDI), which require high expenditure on R&D. Moreover, Hungary strengthened these advantages between 2004 and 2011.

STI Policy Recommendations

What science, technology and innovation policy tools and measures can be used to support changes in these countries’ national innovation systems so that they can effectively compete on international markets with emerging economies in Asia?

There are three dimensions of STI goals: first, unified goals for all EU11 countries; second, individual goals that address specific weaknesses of individual countries; third, wider STI policy goals related to further institutional improvements of the S&T sector.

The policy goals can be the same for the whole EU11 group as they address the similar problems and challenges that exist in all countries. They include in particular:

• the restructuring of R&D financing sources to increase the role of the business sector in total R&D spending to a level no lower than the EU average of 54%;

• growth of public investment in order to support human capital development (supporting improvements of education quality, incentives for young researchers to develop their knowledge and skills, increasing the excellence and internationalization of universities and public research organizations, incentives for publishing internationally and co-publications and co-patenting with foreign partners);

• fostering a better environment for research and business in order to enable closer collaboration between universities and businesses (for example, through public-private partnerships or through the new R&D programs designed with the requirement of business sector involvement);

• facilitating knowledge transfer from abroad and integrating FDI policy into innovation policy;

• strengthening local absorptive capacities through education and training; • increasing the involvement of researchers from EU11 countries in the EU’s Horizon

2020 program (re-defining the rules of participation in the EU programs, including the level of daily rates assigned for individual countries; creating stronger linkages between Horizon 2020 and Cohesion Policy funds;

• further development of the regional dimension of R&D and closer collaboration between national and regional authorities responsible for STI policy design and implementation.

However, when designing STI policy it should be taken into account that this group of countries is not homogenous with regard to the progress in innovation system restructuring. Therefore, there is a need to introduce in the EU a partially differentiated approach to STI policy across countries. Together with some unified policy goals for all EU countries, it might be useful to formulate policy goals tailored for different sub-groups of countries. The question

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is what criteria should be used to define these sub-groups? There are at least two that could be taken into account. The one is the country’s level of development and its current innovation performance (measured by the SII, for instance). The other is the technological profile of the country. The latter criterion is linked to the ability to create competitive advantages in foreign trade. To build and strengthen comparative advantages in foreign trade, tailored STI policy targets need to be systematically linked with the modernization of industries, in particular strengthen those belonging to country’s technological specialization. For most EU11 countries, a key challenge is to move up the value chain and reshape their technological and trade specialization in favor of higher value added activities and strengthen their comparative advantages in goods produced by marketing-driven or technology-driven industries. The advantages in trade of products of these industries are relatively sustainable because the goods of technology-driven industries are difficult to imitate, while sales of products of marketing-driven industries depends on consumer brand loyalty. Therefore, strategic priorities for S&T specialization should be differentiated for individual countries in order to establish specific production profiles linked to human capital availability and developments. Wider STI policy goals should also include further institutional changes in national innovation systems, combined with efforts to create more open and flexible organizational structures in research organizations that are focused on individuals and their international mobility.

References EC, (2013a), Research and Innovation performance in EU Member States and Associated countries. Innovation

Union Progress at Country Level, EC, Directorate-General for Research and Innovation, European Commission, Brussels.

Heiduk G., McCaleb A., (2012), The Role of Openness in China’s Post-Crisis Growth Strategy: Implications for the EU, Warsaw School of Economics, Warsaw.

Matkowski Z., Próchniak M., (2013), Real Income Convergence, in: M.A. Weresa (ed.) (2013), Poland. Competitiveness Report 2013. National and Regional Dimensions, Warsaw School of Economics Press, pp. 44-64.

OECD, (2007), Moving up the Value Chain: Staying Competitive in the Global Economy, OECD, Paris. Peneder M., (1999), The New WIFO Taxonomy Of Manufacturing Industries, WIFO Working Papers No. 114,

Vienna.Rauch M., Sommer-Ulrich J., (2012), Participation of the Central and Eastern European EU Member States

in the 7th Framework Programme: Analysis, Evaluation, Recommendations, Fraunhofer, MOEZ, Leipzig. Tan Y., (2011), Chinnovation. How Chinese Innovators are Changing the World, John Wiley & Sons,

Singapore. �

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Introduction

Nowadays economic development has become more and more knowledge based due to rapid changes in science and technology. The goal of the European Union’s Europe 2020 strategy is to achieve smart, sustainable and inclusive economy. To achieve this ambitious goal, the EU requires more innovation and a better system of knowledge diffusion (EC, 2013). It is particularly important for catching up countries, like the new Member States from Central and Eastern Europe (EU11) as all these countries have innovation performance, measured by the Summary Innovation Index (SII), below the EU average (EC, 2013). However, the group of the EU11 is not homogenous in terms of innovation performance. Slovenia and Estonia are relatively more advanced and they belong to the ‘Innovation Followers’. The Czech Republic, Slovakia, Hungary and Lithuania are ‘Moderate Innovators”, while Poland, Latvia, Bulgaria and Romania constitute the least innovative group in the EU called ‘Modest Innovators’ (EC, 2013). Therefore, for these countries the ability to innovate, or at least to get access to the new knowledge and imitate and/or adapt new solutions, seems to be increasingly important.

Why FDI can be important for innovation in the EU1161?

Foreign direct investment (FDI) could be a source of innovative ideas and concepts. To catch-up, the EU11 countries could improve their innovative potential, using imitation and adaptation of achievements invented abroad. Therefore, the crucial issue is to promote the knowledge transfer and spillovers from FDI through appropriate policy. FDI flows to the EU11 countries have been increasing during the last two decades and they have speeded up after the EU’s eastern enlargement in 2004. By the end of 2011, the total FDI stock located in the EU11 countries amounted to €510 billion, growing more than twice since EU’s eastern enlargement in 2004. Poland has been the leader among the EU11 countries with regard to the value of foreign investment stock (having nearly one third of the total FDI stock absorbed by the EU11 group), followed by the Czech Republic (18.5% in 2011), Hungary (12.5% in 2011) and Romania (10.8% in 2011).

The benefits of FDI for the host country’s innovation performance can arise from the direct exploitation of transferred asset and occur indirectly as a result of the demonstration- imitation effect, the competition effect, the foreign linkage effect and the training effect (technology and knowledge spillovers). Another important aspect of the FDI impact on innovation can be related to outward FDI. The outflow of capital from the EU11 countries has been increasing rapidly since their accession to the EU (a six fold increase since 2004). However, the outward FDI stock from the EU11 is still more than five times lower than inward FDI stock in the EU11 (€87.7 billion compared to €510 billion in 2011). Therefore, the focus here is on the role of inward FDI in shaping innovation performance in the EU11.

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61 The EU11 group consists of 11 post-socialist countries that joined the European Union in 2004 (the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia), in 2007 (Bulgaria and Romania), and in 2013 (Croatia).

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When we consider FDI flows to the EU11 group of countries in the context of their innovation performance, a few questions arise:

• To what extant is the FDI inflow to the EU11 countries accompanied by technology transfer?

• How FDI inflows impact innovation in the EU11? • What are the appropriate policies (at the European level and national level) that can

help the EU11 countries not only to attract FDI but also to benefit from these inflows?

Experience of the EU11 in benefiting from FDI

There are a lot of studies on technology transfer through FDI and knowledge spillovers from FDI in the EU11 countries. They are mostly based on firm level statistical data sets often supported by the qualitative data collected via questionnaires.

The group of the EU11 is not homogenous with regard to FDI absorption and FDI contribution to innovation and competitiveness. In 2011, five countries from the EU11 were in the group of the top ten host economies with the largest contribution by FDI to development and competitiveness: Hungary, the Czech Republic, Romania, Poland and Estonia. With regard to FDI contribution to R&D expenditures, the best performers were Hungary and the Czech Republic, followed by Romania and Poland. In Estonia, Bulgaria and Latvia contribution of FDI is lower than the ratio of FDI stocks to GDP. This may indicate that FDI potential is not fully used in these countries and shows that a host country’s policy matters for FDI impacts. Therefore, the focus of FDI policy should be not only on attracting FDI, but also on benefiting from it.

The results of the studies on technology transfer and spillovers from FDI in the EU11 countries are mixed. There is some evidence of technology transfers and spillover effects (both positive and negative) in the EU11 countries with results depending on the country analyzed and the period taken into account. The main conclusion that can be drawn from the research is that benefiting from FDI (technology transfer, R&D and innovation activity and spillovers) does not occur automatically. Policy interventions are indispensible.

Policy recommendations In new EU member states from Central and Eastern Europe there is still a huge potential to use foreign direct investment as a channel of technology transfer and as a way of upgrading innovation performance through knowledge spillovers. To support innovation through FDI in the EU11 countries, a new approach to investment policy making is essential. It is important to ground investment policy in innovation strategy taking into account not only attracting FDI, but also maximizing FDI benefits. To use FDI for facilitating innovation in the EU11, the goals of policy interventions could be as follows:

1. Setting out strategic priorities with regard to attracting FDI on the basis of a thorough analysis of the country’s current innovation performance and comparative challenges. In particular:

103

− Determining investment priorities taking into account the potential for the transfer and diffusion of appropriate technologies and know-how (e.g., attracting FDI to a country’s hot spots in key technologies),

− Attracting R&D related FDI through adequate policies, demand R&D measures, tax benefits related to R&D activity and intellectual property regime,

− Identifying areas for reinforcement of public and private co-investment (including a regulatory framework for public-private partnerships).

2. Maximizing direct and indirect FDI impact on innovation. In particular:

− Supporting innovation activity of foreign affiliates (programs linked to the use structural funds, such as grants for market uptake of research results),

− Encouraging foreign subsidiaries to generate business linkages and to stimulate local enterprise development (facilitation policies including access to finance),

− Programs encouraging research cooperation between foreign subsidiaries and local universities,

− Incentives for foreign investors to assist in technology upgrading of local SMEs,

− Incentives that encourage firms (foreign and local) to offer training to employees in areas crucial in the country’s policy on human resource development.

4. Linking incentives offered to firms to performance requirements.

5. Supporting the development of the absorptive capacity of local entities by:

− Increasing public investment in education and training,

− Increasing public investment in research infrastructure, business incubators etc.,

− Incentives for industry-driven R&D and research commercialization.

6. Creating methodology for measuring the effectiveness of FDI policy focused on innovation impact. Indicators that measure the impact of investment on innovation may include:

− High-tech export generation by foreign affiliates,

− FDI-firms’ share in generation of highly skilled employment,

− Share of locally produced input in total input of foreign affiliates,

− ISO certificates achieved by foreign affiliates,

− Local patenting activity of foreign affiliates,

− Collaboration of foreign affiliates with local partner in innovation activity.

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Dep. Variable: Innovation Performance Product Only Process Only Product&Process Single Mode 0.403*** 0.143* 0.437*** (0.07) (0.07) (0.07) Dual Mode 0.719*** 0.317*** 0.848*** (0.08) (0.08) (0.08) Triple Mode 1.002*** 0.104 0.936*** (0.15) (0.20) (0.14)

Firm age, fixed firm size effects, intermediate producers, country, sector dummies, foreign group, foreign competition included as well as

R&D, Human_capital and ICT investments.

Obs 14432;Pseudo R-square 0.108;Chi-square 2877.762 * p<0.10, ** p<0.05, *** p<0.01

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Box 2: Econometric analysis of the international involvement and sensitivity to the crisis

We analyse the firm characteristics conducive to a higher crisis sensitivity in a multivariate fashion using on ordered probit with the categories being: no change in turnover, <10%, 10-30%, > 30% loss in turnover. The use of multiple internationalization modes are our key firm characteristics of interest. Simultaneously we correct for firm size, age, foreign ownership, competition from abroad, intermediate status, as well as sector and country affiliation. We also correct for the presence of inputs for innovation (R&D, Human Capital, ICT). Table B.2 presents the results.

Correcting for other firm characteristics, there is no significant difference in crisis sensitivity for single, dual or triple mode internationalisers.

A significant driver of crisis resilience is the firms’ strategy in terms of innovation (R&D as a proxy) and human capital: those firms with a higher share of university graduates, a higher share of investment in R&D and ICT are less likely to experience a reduction in turnover or only smaller reductions in turnover.

Table B.2: Ordered Probit on Turnover change during the crisis Dep. Variable: Turnover change

Single Mode 0.030 (0.03) Dual Mode 0.031 (0.03) Triple Mode 0.049 (0.06) r_d -0.044* (0.02) human_capital -0.670*** (0.10) high_skill -0.142*** (0.04) ICT -0.060** (0.02)

Firm age, fixed firm size effects, intermediate status, country, sector dummies, foreign group, foreign competition included as well

Obs 11152 Pseudo R-square 0.059 Chi-square 1741.147 * p<0.10, ** p<0.05, *** p<0.01 Note: Small firms: 20-49 employees; Medium-sized firms: 50-249 employees; Large firms: above 250 employees.��

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6. Main findings

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7. Some Policy Implications

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ANNEXES

EUROPEAN COMMISSION DIRECTORATE-GENERAL FOR RESEARCH & INNOVATION

Directorate C - Research and Innovation C.2 - Relations with stakeholders (ERIAB)

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The High-level Economic Policy Expert Group 'Innovation for Growth - i4g' was established in 2011 to provide the best possible economic advice on research and innovation (R&I) policy to the Commission. The group is composed of 15 high level innovation economists who meet bi-monthly. The mandate of i4g shall last the full term of the Commissioner.

Mission and Objectives

The mandate and the mission of the expert group relate to the policy implementation of the Innovation Union. The group works in close interaction with the Commission services and take into account the evolving discussions at political level. � Provision of independent economic advice on research and innovation (R&I) policy to the

Commission; � Assessment of socio-economic impact and of the potential of R&I; � Review of best R&I cases and practices; � Consideration of the economic benefits of the European Research Area (ERA) and exploration of

related issues. � Exploration and suggestions of new avenues for R&I policies; � Recommendations on how to make the Innovation Union work at European, national and regional

level within a global context; � Advice to the Commission on policies for research and innovation and how to turn them into

economic growth;

Output and Achievements

The Cabinet of the Commissioner for Research and Innovation has exclusive rights to the output of i4g. Annual workshops on 'Research and Innovation Policy' discuss selected i4g outputs with invited Commission services as done at the workshop 'Innovating out of the Crisis', held 28 November 2012 and on the following one 'The changing geography of global R&I – policy implications', held 21 November 2013, chaired by the Cabinet. The Commission supports i4g with studies on issues of

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immediate relevance to research and innovation policy. I4g is assisted by a secretariat (DG RTD Unit C2).

The group provides:

� R&I Policy Briefs – short papers substantiating a policy advice in the field of research and innovation: 1. Innovating out of the Crisis 2. Public Procurement 3. Socio-economic Benefits of ERA 4. Financing Innovation 5. Seed Capital 6. Innovation in Greece 7. Service Innovation 8. Smart Specialisation 9. Risk and Reward of Innovation Policy 10. Research Excellence in EU Universities 11. Innovation and the Public Research Organization in the ERA 12. Promoting young innovative companies in Europe by science 13. Innovation and Institutions 14. Clusters vs. Pipelines for Innovation in Peripheral Areas 15. The notion of Inclusive Innovation 16. A European Initiative for Excellence (EIE) 17. The performance of Public Research Systems and Innovation in European Commission 18. Create a 4% economy for inclusive innovation 19. Innovation System Restructuring in New EU Member States in Competition with Asia 20. FDI and Innovation in Central and Eastern EU Countries 21. Innovative firms in Global Value Chains´

� R&I Policy Papers – elaborated papers and short studies on R&I policy relevant issues:1. Service innovation: measurement of performance gaps and public policy; 2. Best Practice programmes in the Member states to support research and innovation in services (DE, FIN and EPISIS project); 3. Frugal and inclusive innovation; 4. Public Procurement and Innovation in the post-1945 U.S. Economy; 5. Dissemination mechanisms and innovation regimes in high-tech clusters; 6. Changing global value chains of production and its implications for research and innovation policies

� Dedicated i4g Reports –produced by particular i4g Panels:1. Socio-economic benefits of the European Research Area 2. i4g Summary of Policy Briefs Year 2012 3. First Annual i4g Research & Innovation Policy Workshop (2012)

Work Programme 2013/14

The work programme of i4g 2013/2014 foresees four thematic areas 1. Globalisation of research and innovation - R&I

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2. Overcoming the innovation divide: Regional aspects of innovation 3. Inclusive growth 4. Performance of public research systems in view of innovation

Critical issues for research and innovation policies to be worked on will be the impact of global innovation networks driven by multinational corporations and how public policy can shape these networks for the benefit of European citizens. A further issue is the low research and innovation performance of the New Member States as well as that of the Mediterranean Member States as well as the question how to increase the performance of those Member States.

Public events - STOA hearing 'The Performance of Public Research Systems and Innovation in Europe' ,

13 June 2013, Strasbourg, France - I4g/OECD workshop : Global Value chains, global innovation networks and economic

performance, 9-10 September 2013, Paris - I4g/IPTS: Exchange of research results and assistance of the CONCORDi-2013

conference, 26-27 September 2013, Seville - 2nd i4g workshop 'The changing geography of global R&I – policy implications', 21

November 2013, Brussels More info and downloads: http://ec.europa.eu/research/innovation-union/index_en.cfm?pg=expert-groups

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Innovation for Growth – i4g Relation of research & innovation with smart, sustainable and inclusive growth

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European Commission Collection of i4g Policy Briefs 2013 — High Level Economic Expert Group ‘Innovation for Growth – i4g’ Luxembourg: Publications Office of the European Union 2013 — 130 pp. — 17.6 × 25 cm ISBN 978-92-79-33380-4doi:10.2777/43626

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