Analysis of. Patents & Licencing. for European policies. Research and Innovation

Transcription

1 Analysis of Patents & Licencing for European policies Research and Innovation

2 EUROPEAN COMMISSION Directorate-General for Research and Innovation Directorate A Policy Development and Coordination Unit A.4 Analysis and monitoring of national research policies Contact: Sylviane Troger sylviane.troger@ec.europa.eu RTD-PUBLICATIONS@ec.europa.eu European Commission B-1049 Brussels

3 EUROPEAN COMMISSION Analysis of Patents & Licensing for European policies This study was financed under FP7 (Capacities Programme Support for the coherent development of research policies), tender n 2009/S (Study on Measurement and analysis of knowledge and R&D exploitation flows, assessed by patent and licensing data) Produced and written by Bart Van Looy, Caro Vereyen, Julie Callaert (Research Division INCENTIM, MSI, Faculty of Economics & Business), Bart Van Looy (K.U.Leuven), Stefano Breschi & Gianluca Tarasconi (Università Commerciale Luigi Bocconi), Gianluca Tarasconi (CRIOS), Alfred Radauer (Technopolis) 2015 Directorate-General for Research and Innovation

4 Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): (*) The information given is free, as are most calls (though some operators, phone boxes or hotels may charge you). LEGAL NOTICE This document has been prepared for the European Commission however it reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. More information on the European Union is available on the internet ( Luxembourg: Publications Office of the European Union, 2015 ISBN doi: /05522 European Union, 2015 Reproduction is authorised provided the source is acknowledged. Cover Image: Kheng Guan Toh, # , Source: Fotolia.com.

5 Contents 1. TECHNOLOGY IN EUROPE The technological profile and specialisation pattern of (global) regions Exploitation of technological results by the market TECHNOLOGY IN EUROPE COMPARED TO THE REST OF THE WORLD Patterns of Science-Technology Linkage International collaboration and the role of ERA CONCLUSIONS... 25

6 Within this Patents & Licensing (P&L) Project ( ) considerable efforts have been devoted to collecting and analysing data stemming from patent documents. In addition, a large scale survey has been conducted on the licensing behaviour of European firms. Finally, available statistics on trade flows (licensing) have been used to complement indicators and survey data. The combination of these data and indicators allowed to engaging in meaningful analysis of the current European Union (EU) position in terms of technological capabilities, license and knowledge flows (including those from science towards technology) and internationalisation. The following pages provide a comprehensive overview of major findings and insights. 1. TECHNOLOGY IN EUROPE Technological profile and specialisation pattern of (global) regions Thematic Priorities, IPC 35 technology fields and 22 NACE sectors). Despite various data sources and classification approaches, a few robust facts emerged. Main Findings The first major finding pertains to the specialisation patterns of the major world areas. During the period , the ERA is characterised by a marked specialisation in mechanicalrelated areas and technologies (including transport) and less present in ICT 7 and nanotechnologies. Conversely, Asia is specialised in ICT and nanotechnologies while biotechnology, pharmaceuticals and aerospace are less developed. As to the United States, they present a strong specialisation in pharmaceuticals, biotechnology, space and the more service-oriented segments of ICT. The next table shows an overview of the specialisation and de-specialisation patterns by geographical area and by type of classification for the period along the four patent systems. The Patents & Licensing Project provides a broad overview of the most recent patterns of technological specialisation at the level of European countries and global geographical areas (U.S., EU, ERA 2, Asia). The analysis uses data from different patent systems (EPO 3, WIPO 4, USPTO 5 and Triadic 6 ) and reflects three different classifications of patents (FP7 1 This project ran between 2010 and 2014 while Croatia entered the EU in July Therefore, observations/data for Croatia are not comparable to other EU countries and hence not included in the analysis/report. 2 For the purpose of this study, ERA countries includes the EU-28 Member States, EFTA countries (Switzerland, Liechtenstein, Iceland, Norway), Candidate Countries (Turkey, The Former Yugoslav, Republic of Macedonia, Montenegro and Serbia), and Israel. 3 EPO (The European Patent Office) 4 WIPO (The World Intellectual Property Organization) 5 USPTO (The United States Patent & Trademark Office) 6 Triadic concerns corresponding patents that are filed at EPO, USPTO and JPO (The Japan Patent Office) for the same invention, by the same applicant or inventor. 7 ICT (Information and Communications Technology) 4

7 Patterns of specialisation and de-specialisation, by geographical area and type of classification ( ) Areas of FP7 Thematic Priorities IPC 35 NACE sectors ERA Strength Aeronautics Automobiles Construction Technologies Energy Food, Agriculture and Fisheries New Production Technologies Measurement Organic fine chemistry Chemical engineering Environmental technology Handling Machine tools Engines, pumps, turbines Other special machines Mechanical elements Transport Other consumer goods Civil engineering Food products and beverages Sales of textiles Plastic products Non-metallic mineral products General purpose machinery and machine tools Motor vehicles Aircraft and spacecraft Machinery and equipment Weakness Green Energy ICT Nanosciences/ Nanotechnologies Electrical machinery Audio-visual technology Telecommunications Digital communication Basic communication processes Computer technology IT methods for management Semiconductors Optics Office machinery and computers Electricity distribution and control apparatus Electronic components Services for computer and related activities Telecommunication equipment EU Strength Aeronautics Automobiles Construction Technologies Energy Food, Agriculture and Fisheries Measurement Organic fine chemistry Chemical engineering Environmental technology Handling Machine tools Engines, pumps, turbines Other special machines Mechanical elements Transport Other consumer goods Sales of textiles Plastic products Non-metallic mineral products General purpose machinery and machine tools Motor vehicles Aircraft and spacecraft Machinery and equipment Weakness Green Energy ICT Nanosciences/ Nanotechnologies Electrical machinery Audio-visual technology Telecommunications Digital communication Basic communication processes Computer technology IT methods for management Semiconductors Optics Medical technology Office machinery and computers Electricity distribution and control apparatus Electronic components Services for computer and related activities Telecommunication equipment 5

8 Areas of FP7 Thematic Priorities IPC 35 NACE sectors ASIA (JP, IN, CN, KR) Strength Energy Environment Green Energy ICT Electrical machinery Audio-visual technology Telecommunications Basic communication processes Semiconductors Optics Materials, metallurgy Textile and paper machines Office machinery and computers Electrical motors, generators and transformers Electricity distribution and control apparatus Electronic components Weakness Aeronautics Biotechnology Construction Technologies Food, Agriculture and Fisheries Health New Production Technologies Security Space IT methods for management Measurement Biological materials analysis Medical technology Biotechnology Pharmaceuticals Food chemistry Basic materials chemistry Micro-structural/nanotech Chemical engineering Handling Other special machines Furniture, games Civil engineering Food products and beverages Sales of textiles Recorded media and related goods Pharmaceuticals Plastic products Non-metallic mineral products Medical and surgical equipment Aircraft and spacecraft Services for computer and related activities Machinery and equipment U.S. Strength Aeronautics Biotechnology Health New Production Technologies Security Space Computer technology IT methods for management Biological materials analysis Medical technology Biotechnology Pharmaceuticals Basic materials chemistry Micro-structural/nanotech Sales of textiles Recorded media and related goods Pharmaceuticals Medical and surgical equipment Services for computer and related activities Weakness Automobiles Energy Electrical machinery Audio-visual technology Telecommunications Optics Organic fine chemistry Materials, metallurgy Machine tools Engines, pumps, turbines Textile and paper machines Mechanical elements Transport General purpose machinery and machine tools Electrical motors, generators and transformers Electricity distribution and control apparatus Motor vehicles Electrical machinery Telecommunication equipment Source: Università Commerciale Luigi Bocconi, CRIOS Note: Own elaborations on PATSTAT (October 2013). Based on the RTA (Revealed Technology Advantage) index, sectors of relative strength are those with a value greater than one in all four patents systems (EPO, WIPO, USPTO and Triadic) and of relative weakness for values lower than one. Asia includes China, India, Japan and South Korea. European Union and ERA do not include Croatia as at the time of writing this study, Croatia was not yet part of the EU. RTA index is explained on page 13 (see footnote 13b). 6

9 Health Food and Agriculture Biotechnology ICT Nanotechnologies Materials New Production Technologies Construction Technologies Energy Environment Aeronautics Automobiles Other Transport Technologies Space Security Green Energy Focusing on European countries, the following tables illustrate graphically the major fields of relative technological strength and weakness of each country per classification system. Fields of specialisation ( ) and de-specialisation ( ) of EU countries 8 Based on FP7 Thematic Priorities ( ) Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Source: Università Commerciale Luigi Bocconi, CRIOS 8 Note: Own elaborations on PATSTAT (October 2013). Based on the RTA index (along EPO and WIPO systems), a country is relatively specialised in a field if the value is greater than one and de-specialised if lower than one. Please note that the RTA index (defined in footnote 13b, page 13) has to be interpreted with great caution for countries with a low number of patents. 7

10 Electrical engineering Scientific instruments Pharma- Biotechnology Chemistry Mechanical engineering Other technologies Fields of specialisation ( ) and despecialisation ( ) of EU countries Based on IPC 6 macro sectors ( ) 9 Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Source: Università Commerciale Luigi Bocconi, CRIOS Patterns 10 in EU countries can be outlined as follows: Austria is relatively specialised in mechanical related fields, such as machine tools (I), materials and metallurgy (I), construction technologies (F), other transport technologies (F), electrical machinery (N), general purpose machinery and machine tools (N); while it is relatively de-specialised in technologies related to ICT, drugs and scientific instruments, particularly telecommunications (I), digital communication (I), ICT (F), space (F), telecommunication equipment (N), electricity distribution and control apparatus (N) and office machinery and computers (N). Belgium shows a relative strength in fields related to drugs and chemistry, such as macromolecular chemistry (I), food chemistry (I), biotechnology (I,F), and pharmaceuticals (N), as well as in areas broadly associated to mechanical technologies, such as textile and paper machines (I), other special machines (I), construction technologies (F), and machinery and equipment (F). Its relative weaknesses are concentrated in most fields of ICT, in particular telecommunications (I), digital communication (I), computer technology (I), space (F), aeronautics (F), security (F), ICT (F), electricity distribution and control apparatus (N), services for computer and related activities (N), telecommunication equipment (N). Bulgaria presents a pattern of relative specialisation in food-related sectors, such as food chemistry (I), food and agriculture (F), food products and beverages (N), and in some niches related to production processes and environment, such as thermal processes and apparatus (I), plastic products (N), environmental technology (I) and environment (F). It is 9 Note: Own elaborations on PATSTAT (October 2013). Based on the RTA index (along EPO and WIPO systems), a country is relatively specialised in a field if the value is greater than one and de-specialised if lower than one. Please note that the RTA index (defined in footnote 13b, page 13) has to be interpreted with great caution for countries with a low number of patents. 10 Abbreviations indicate (I) technology class along the IPC 35 classification, (F) field defined along the FP7 Thematic Priorities, (N) sector defined along the NACE classification. 8

11 instead particularly de-specialised in the broad areas of ICT and scientific instruments, such as digital communication (I), optics (I), medical technology (I), ICT (F), security (F), electrical machinery (N), telecommunication equipment (N), and in some areas of chemistry, particularly organic fine chemistry (I), macromolecular chemistry (I), biotechnology (I), materials (F). Croatia was not yet part of the EU at the time of writing the section on specialisation patterns. Cyprus is specialised in a few niches, among which other consumer goods (I), engines, pumps, turbines (I), civil engineering (I), IT methods for management (I), environmental technology (I), construction technologies (F), green energy (F), and environment (F); it is instead despecialised in most areas of ICT and chemistry, with the exception of environment-related technologies. Czech Republic shows a relative technological strength in chemistryrelated areas, e.g. organic fine chemistry (I), pharmaceuticals (I,N), in textiles, e.g. textile and paper machines (I), sales of textiles (N), and in transport-related technologies, e.g. transport (I), construction technologies (F), automobiles (F), other transport technologies (F), motor vehicles (N). It is instead strongly de-specialised in all technologies related to ICT and scientific instruments: taking for example the IPC classification, the Czech Republic presents a value of the RTA lower than one both at the EPO and at the WIPO in all eight technological fields classified in the macro area of Electrical Engineering. Denmark is characterised by a strong presence in food and biotechnology sectors. It shows relative technological strengths in food chemistry (I), biotechnology (I), pharmaceuticals (I), food and agriculture (F), biotechnology (F), food products and beverages (N), pharmaceuticals (N). On the other hand, it appears strongly de-specialised in most fields of information and communication technologies and in materials and basic chemistry. Among the fields with the lowest RTA, one finds ICT (F), nanotechnologies (F), materials (F), electronic components (N), office machinery and computers (N). Estonia presents a pattern of strong specialisation in food and biotechnology, similarly to Denmark. Among the fields presenting a large value of the RTA, we find analysis of biological materials (I), food chemistry (I), biotechnology (I, F), food and agriculture (F), food products and beverages (N). Other areas of strength are found among scientific instruments, e.g. control instruments (I), measurement instruments (I), instruments and appliances (N), medical and surgical equipment (N). The areas of weakness comprise both the ICT, which is the field with the lowest value of the RTA according to the Thematic Priorities classification, and mechanical engineering. Finland is one of the few European countries to present a pattern of strong specialisation in ICT. Among the fields of greater specialisation, we find digital communication (I), telecommunications (I), security (F), ICT (F), telecommunication equipment (N), office machinery and computers (N). At the same time, Finland is strongly de-specialised in most areas of chemistry and mechanical engineering. Among the European countries, Finland is the one presenting the highest levels of concentration of relative technological advantages in a limited number of fields. France shows a pattern of specialisation in space-related technologies, as well as in fields related to transport. Among the fields with a high value of the RTA, one finds transport (I), engines, pumps, turbines (I), space (F), aeronautics (F), automobiles (F), aircraft and spacecraft (N), motor vehicles (N). The areas of relative de-specialisation include pharmaceuticals and biotechnology, e.g. health is the field showing the lowest value of RTA among the Thematic Priorities fields, as well as 9

12 some ICT-related segments, e.g. IT methods for management (I), semiconductors (I), services for computer and related activities (N), and electronic components (N). Germany is characterised by a pattern of strong specialisation in mechanical engineering and transport technologies. The fields with a high RTA comprise mechanical elements (I), transport (I), engines, pumps, turbines (I), machine tools (I), automobiles (F), construction technologies (F), motor vehicles (N), general purpose machinery and machine tools (N), machinery and equipment (N), electrical motors, generators and transformers (N). The fields of relative de-specialisation belong to the areas of ICT and health: telecommunications (I), IT methods for management (I), digital communication (I), audio-visual technology (I), ICT (F), health (F), telecommunication equipment (N), office machinery and computers (N), services for computer and related activities (N). Greece presents a pattern of specialisation in some niches of mechanical engineering and foodrelated technologies. The areas presenting the highest values of RTA include machine tools (I), civil engineering (I), other special machines (I), food chemistry (I), construction technologies (F), food and agriculture (F), non-metallic mineral products (N), food products and beverages (N). The relative technological weaknesses concentrate instead in the ICT, particularly digital communication (I), semiconductors (I), ICT (F), telecommunication equipment (N), office machinery and computers (N), scientific instruments and other areas of chemistry and biotechnology. Hungary is specialised in health and food related technologies. Among the fields of stronger specialisation, one finds organic fine chemistry (I), pharmaceuticals (I), food chemistry (I), health (F), food and agriculture (F), pharmaceuticals (N), food products and beverages (N). On the other hand, it appears relatively weaker in all areas of ICT, in scientific instruments and in the fields of transport technologies. Ireland is characterised by a relative specialisation in some specific niches related to software, i.e. IT methods for management (I), services for computer and related activities (N), in scientific instruments and health, i.e. medical technology (I), control instruments (I), pharmaceuticals (I), health (F), medical and surgical equipment (N). Its relative technological weaknesses are found in mechanical technologies, especially those related to transport: the four fields exhibiting the lowest values of RTA are aeronautics (F), space (F), automobiles (F), other transport technologies (F). Italy presents a strong specialisation in traditional industries, in mechanical engineering (particularly, machine tools and packaging) and in transport related technologies: handling (I), other consumer goods (I), furniture, games (I), thermal processes and apparatus (I), civil engineering (I), other special machines (I), machine tools (I), other transport technologies (F), construction technologies (F), food and agriculture (F), plastic products (N), machinery and equipment (N), nonmetallic mineral products (N). The areas of relative de-specialisation include, in particular, all fields of ICT, biotechnology and pharmaceuticals. Latvia is specialised in health, i.e. organic fine chemistry (I), pharmaceuticals (I), health (F), pharmaceuticals (N), and in some specific niches of food technologies, i.e. food chemistry (I), and materials technologies, i.e. surface technology, coating (I), materials (F), non-metallic mineral products (N). It is instead despecialised in most of areas of ICT, in scientific instrumentation and in most fields of mechanical engineering. Lithuania appears to be strongly specialised in biotechnology and food-related technologies, i.e. food chemistry (I), biotechnology (I), biotechnology (F), food and agriculture 10

13 (F), food products and beverages (N), as well as in scientific instruments, i.e. optics (I), measurement instruments (I), medical technology (I), instruments and appliances (N), and in some niches related to aerospace. On the other hand, it appears particularly weak in ICT and in materials technologies. Luxembourg is specialised in materials technologies and in technologies related to the transport industry, i.e. materials, metallurgy (I), thermal processes and apparatus (I), transport (I), surface technology, coating (I), engines, pumps, turbines (I), automobiles (F), space(f), other transport technologies (F), motor vehicles (N), non-metallic mineral products (N), plastic products (N). The fields of de-specialisation cover the whole spectrum of ICT, biotechnology and various areas of chemistry. Malta is specialised in quite traditional technologies: the fields exhibiting the highest values of the RTA index include civil engineering (I), other consumer goods (I), handling (I), machine tools (I), furniture, games (I), construction technologies (F), nonmetallic mineral products (N), plastic products (N). Yet, it also shows a presence in some advanced niches, such as micro-structural and nanotechnology (I) and new production technologies (F). Netherlands shows a pattern of specialisation in ICT particularly, basic communication processes (I), audio-visual technology (I), computer technology (I), semiconductors (I), ICT (F), office machinery and computers (N), electronic components (N) in food-related technologies, in particular, food chemistry (I), food and agriculture (F), food products and beverages (N) are the fields presenting the highest values of RTA, and in scientific instruments (except medical technology), i.e. optics (I), measurement instruments (I), instruments and appliances (N). The areas of relative de-specialisation include all technologies related to transport industries, i.e. engines, pumps, turbines (I), machine tools (I), transport (I), mechanical elements (I), aeronautics (F), automobiles (F), space (F), electrical motors, generators and transformers (N), electricity distribution and control apparatus (N), motor vehicles (N), as well health and pharmaceuticals. Poland presents a mix of specialisations in quite traditional sectors (civil engineering (I), construction technologies (F), nonmetallic mineral products (N) are the fields showing the highest values of RTA), in some emerging areas related to environmental technologies, i.e. environmental technology (I), environment (F) and green energy (F), and in technologies related to the food industry, i.e. food chemistry (I), food and agriculture (F), food products and beverages (N). Its major weaknesses concern the whole area of ICT and scientific instruments. Portugal is strongly specialised in construction technologies civil engineering (I) and construction technologies (F) are the areas presenting the highest values of RTA, but it also shows a presence in biotechnology (I), pharmaceuticals (I, N), health (F), as well as in some fields related to food and textile industries, i.e. food chemistry (I), food and agriculture (F), food products and beverages (N), other special machines (I), sales of textiles (N). On the other hand, it is strongly de-specialised in all areas of ICT and in several areas of chemistry. Romania presents a specialisation in construction technologies civil engineering (I) and construction technologies (F) are the areas presenting the highest values of RTA, in some traditional industries, such as other consumer goods (I), materials, metallurgy (I), furniture, games (I), Non-metallic mineral products (N), but interestingly also in technologies related to the aerospace industry, i.e. engines, pumps, turbines (I), aeronautics (F), aircraft and spacecraft (N). At the same time, it looks despecialised in most fields of chemistry and ICT. 11

14 Slovakia presents a pattern of specialisation in quite heterogeneous fields: the four fields presenting the highest values of RTA are, respectively, thermal processes and apparatus (I), IT methods for management (I), engines, pumps, turbines (I), civil engineering (I), mechanical elements (I); other transport technologies (F), environment (F), green energy (F), construction technologies (F); sales of textiles (N), services for computer and related activities (N), plastic products (N), aircraft and spacecraft (N). Despecialisation is found especially in ICT (with the only exception of IT services) and scientific instruments. Slovenia exhibits a strong presence in health technologies: pharmaceuticals (I), health (F) and pharmaceuticals (N) are the areas presenting the highest values of the RTA index. On the other hand, it is absent from ICT, scientific instruments and most mechanical engineering technologies. Spain is characterised by a specialisation in technologies related to the transport industry, i.e. transport (I), aeronautics (F), other transport technologies (F), aircraft and spacecraft (N), but also in food technologies, i.e. food chemistry (I), food and agriculture (F), food products and beverages (N), in some segments of mechanical engineering as well as in biotechnology and pharmaceuticals. The areas of de-specialisation concern the whole field of ICT, scientific instruments and materials technologies. Sweden is characterised by a rather strong specialisation in telecommunications, i.e. digital communication (I), telecommunications (I), basic communication processes (I), ICT (F), Telecommunication equipment (N). It also shows a relative presence in medical technologies, i.e. medical technology (I), control instruments (I), medical and surgical equipment (N), in transport technologies, i.e. transport (I), automobiles (F), motor vehicles (N), and in machine tools, i.e. machine tools (I), general purpose machinery and machine tools (N). Its major areas of weakness cover electronic components, i.e. semiconductors (I), Electronic components (N), electrical machinery, i.e. electrical machinery (I), electricity distribution and control apparatus (N), biotechnology and most areas of chemistry. United Kingdom shows a quite strong specialisation in scientific instruments and aerospace technologies, i.e. control instruments (I), measurement instruments (I), analysis of biological materials (I), aeronautics (F), aircraft and spacecraft (N), instruments and appliances (N), medical and surgical equipment (N), but also in pharmaceuticalsbiotechnology, i.e. pharmaceuticals (I), health (F), biotechnology (F), pharmaceuticals (F). The major areas of technological weakness regard ICT (particularly, electronic components), transport technologies, i.e. automobiles (F) and motor vehicles (N), and materials technologies. 12

15 Second, we compared the distribution of patents across technological fields with countries in other geographical areas 11. This analysis shows the emergence of well-defined groups of countries sharing similar profiles of specialisation. A first group of countries strongly specialised in ICT and nanotechnologies consist of China, South Korea and Finland (with Sweden and Netherlands located in the proximity of this cluster). A number of European countries (Germany, Italy, Austria, and Norway) are located in a cluster characterised by a specialisation in traditional, mechanical-oriented technologies. France, UK and Sweden, on the other hand, display similarities with the United States and Japan. Finally, clusters consisting of smaller countries specialised in niche areas, such as chemicals, food and health, are present as well (Belgium, Switzerland, Denmark). Moreover, we observe a tendency for countries to group within clusters while the distance between clusters tends to increase (from the first to the second half of the 00s) signalling the formation of several sub-european clusters, each specialised in specific areas. The patterns of specialisation by country observed above suggest that the EU and the ERA tend to cover a broader spectrum of technologies compared to other geographical areas. This is confirmed by the two tables below. Results reported there show that the EU and the ERA are characterised by a higher degree of dispersion of their technological strengths across technological areas than the United States and Asia. Moreover when adopting a longitudinal perspective, Asia and the United States show a tendency towards concentrating their technological efforts in a narrower number of domains while the opposite pattern seems to hold for the ERA, at the least at the EPO For this analysis, smaller countries in terms of total number of patents were excluded in order to only focus on major patenting countries. 12 Please note that results from longitudinal analysis have to be taken with some caution. For the specialisation analysis, we used the PATSTAT October 2013 version. As a matter of fact, the number of patent applications dropped quite significantly after This is the combined effect of right truncation of data, due to various lags in the Diversification Index 13, FP7 Thematic Priorities according to EPO AREA Trend (%) EU CANDIDATE EFTA ERA ASIA (CN,IN,JP,KR) USA China India Japan South Korea Source: Università Commerciale Luigi Bocconi, CRIOS publication of patent applications, and of the economic crisis. 13 The Diversification Index (DIV) is defined as follows: 1 DIV i,t =. Where μ RTA is the mean of the CV RTAi,T = μ RTAi,t σ RTAi,T RTA 13b index of area i at time t across technological sectors and σ RTA is the standard deviation of the RTA distribution of the same area. Low values of the index indicate concentration of an area s profile of specialisation in few technological fields: in this situation, the geographical area presents very high values of RTA in some particular technological sectors and very low RTA values in others technological sectors. Conversely, high values of the index imply that RTA values across technological classes are closer to the mean, thereby suggesting a more diversified profile of technological specialisation. 13 b The "Revealed Technological Advantage (RTA)" is the most frequently used specialisation index: RTA ij = Relative specialisation in technology class i for country or region j = (P ij /Σ i P ij ) / (Σ j P ij /Σ ij P ij ) with i = 1... N (N = the number of technology classes in the study); with j = 1... M (M = the number of countries or regions in the study) with P ij = the number of patents in technology class i in country or region j This index represents the share of country or region j in the total number of patents in technology class i, vis-à-vis the share of country or region j in the total number of patents of all countries or regions over all technology classes. The computations of the RTA s are based on patent results from PATSTAT (October 2012). 13

16 Diversification Index, FP7 Thematic Priorities according to WIPO AREA Trend (%) EU CANDIDATE EFTA ERA ASIA (CN,IN,JP,KR) USA China India Japan South Korea Source: Università Commerciale Luigi Bocconi, CRIOS 1.2 Exploitation of technological results by the market Within the P&L project, considerable efforts have been devoted to map and analyse knowledge flows and the exploitation of results of technological developments by the market. These efforts rely on patent citation analysis, trade data analysis and a survey on licensing behaviour of patenting firms. The three sources of data have their own unique set of advantages and challenges and depict also three different types of knowledge flows, respectively exploitations of technological results by the markets: Patent citation data is highly useful for identifying potentially valuable patents. The knowledge flows that are traced are those where there is evidence that a patent document is relevant for follow-up inventors. Patent citation works better, if the underlying patenting and examination process is of high quality. Still, considerable efforts have to be made to process the patent data beforehand to reach meaningful results. Patent data is, however, also complete, i.e. citation analysis can be performed on the whole set of available patents. Analysing trade data can trace the aggregate monetary value of in- and out-licensing between firms and other economic entities across countries. However, the biggest issue is that there is no dataset available that would allow differentiating between technology/patent-based licensing and other forms of nontechnology licensing (such as franchises, or TV shows). In addition, the data available is in practice not complete, leading to a whole range of robustness issues. Despite of these limitations and in conjunction with the other two sources of data, it is possible to derive some useful insights also for the specific case of technology and patent licensing. Survey data was the most direct means to obtain information that provides answers directly from firms regarding technology (patent) licensing practices. While the survey allows to creating a broader picture, representativeness becomes an issue. For licensing this is a particular challenge, because there are few to none known parameters regarding the population of patentees that engage in technology licensing and hence also very few possibilities to check the sample against the overall population. The realised sample consists of 330 European firms and has the following characteristics: As expected, there is a rather strong bias towards firms that are actively involved in licensing practices. Our sample is leaning towards larger firms: almost two thirds of the firms (63.3%) had more than 250 employees in 2011 which should come as no surprise, given that we contacted primarily the top-300 applicants in different technology fields (n=35). The number of SMEs 14

17 (all firms with less than 250 employees) in this sample amounts to 113. In this report, we will primarily distinguish between SMEs and large firms when performing analyses according to firm size. The distribution of the firm respondents by country reflects the clear domination of Germany as a source country for patent applications in Europe. Around 34% of the responses are from German firms, which can be expected given the size of the German market and the important role German firms play as applicants for patents at the EPO. With regard to industry breakdowns, the Technology-ICT class accounts for most of the firms (21%) in the sample, while the industries oil, gas, basic materials and utilities accounts for the least number of firms (13%). Firms in industrial engineering represent 18% of the analysed sample. For 282 firms we have been able to match them with PATSTAT. In total, these firms own 79,812 patents (status applied for or granted) at the EPO. The analysis indicates that one cannot assume that small numbers of patents (per assignee) indicate an SME. Main Findings One of the most striking is the strong U.S. position that has become evident in all three analysed datasets. In terms of licensing revenue streams between countries, the U.S. is by far the biggest net exporter, while the EU28 countries are a net importer. The patent citation analysis shows that while Europe is in a favourable position vis-à-vis the U.S. in terms of highly cited patents (HCP s), this is not the case for highly diffusing patents (HDP s), that is patents which signal knowledge flows into several, different, technology fields. The lead of the U.S. is considerably larger in terms of HDPs than the European lead in HCPs. The survey results show, in addition, that apart from intra-european trade with licensing, the U.S. is by far the most important trading partner for European technology/patent licensors. Asia plays a less significant role when it comes to highly cited patents or highly diffusing patents. The available data indicate that Asia can be currently equated mostly to Japan. According to the survey results, comparably few firms in Europe currently out-license their patents to Asia. We see most of this Asian-connected out-licensing activity in electronics-related sectors. Two aspects became evident in Europe: the strong position of Germany, particularly in absolute terms, and a considerable difference between the old EU15 countries and the New Member States (accession in 2004 or later). The latter hardly plays any significant role in the trade of licensing revenues or in being source countries for patented knowledge and valuable patents, which would be otherwise indicated by HCP s and HDP s data. In line with previous findings concerning New Member States, local industry hardly has the technological capacity (and the respective economic development stage) to file for a larger number of patents in different jurisdictions. Particularly for smaller firms, the costs of international patent protection seem to be prohibitive. There are sector-specific differences observable in the different datasets. HCP data indicates relative advantages for the EU28 for example in sectors related to traditional (mechanical) engineering. The survey results indicate different outlicensing behaviour patterns across industries. The health care sector is different in terms of motivational patterns to out-license patents (primarily earning revenues, but also to enable joint R&D and innovation) and the perception of barriers (there is much less concern overall about barriers to out-licensing) than other sectors. Licensing seems to be rather commonplace, and patents a currency in dealing with other companies. By contrast, firms in industrial engineering sectors see more benefits in acting on perceived infringement and have a stronger perception of barriers. Across 15

18 all industries with the exception of health care the most critical barrier to out-licensing mentioned is the fear of potential loss of technological edge, followed by difficulties finding the right licensing partners. Barriers perception to patent out-licensing, by firm size (as arithmetic means of answers ranging from 1 (unimportant) to 4 (very important) Potential loss of competitive/technological edge Difficulties identifying right partners Difficulties to reach agreements on terms other than the price Technology not developed enough Difficulties to monitor or enforce licensing agreement Price offered too low Lack of info on how to price the license Costs for external support Insufficient size of own patent portfolio Non-tariff barriers in legal system Lack of own know-how on how to draft licensing agreement Internal organisational issues Non-availability or lack of quality of external support Large firms (250 or more employees) Small firms (10 to 49 employees) Medium-sized firms (50 to 249 employees) Micro firms (up to 9 employees) Source: Technopolis Survey n (micro-firms**) = 12-19, n (small firms) = 21-29, n (medium-sized firms) = 39-43, n (large firms) = (** caution with interpretation, n between 12 and 19) A breakdown by firm size reveals also differences, for example with respect to the perception of barriers (see figure above). Overall, the most prominent barrier to out-licensing fear of potential loss of the competitive edge is particularly pronounced with large firms and medium-sized firms. Difficulties in identifying the right partners are somewhat more pronounced with smaller firms than with large entities. Microfirms seem to have particularly more 16

19 know-how issues in the licensing field than firms in the other size classes, and also perceive the costs for external support to be a more outspoken barrier From our analysis, two conclusions can be drawn for policy makers. First, industry-specific approaches to fostering the knowledge transfer via patents and licensing may be warranted. Secondly, and this is a deviation from previous studies on licensing, policy support to foster licensing may not be needed for a larger group of firms in selected technology fields. After all, firms may hold patents simply for the purpose of protecting the products/services they offer or their respective production processes. There might be hence a good reason for firms not to engage in the licensing of their technologies. The available data also indicates a growing importance of licensing. Trade data shows a steady increase of licensing volumes in the trade between the main regions and countries over time (particularly for the U.S. and the EU28); the survey results indicate in net terms a growing number of licensing deals and a growing volume of licensing revenue across all industries and sectors. Qualitative feedback received from the survey indicates that licensing as such may not present overall too many issues for firms and that more serious problems are seen in the underlying patent system. Some insights were observed based on the three datasets analysis: Strong position of the U.S.: The study showed that U.S. remains an important player in this area. This has, for us, two implications: on one hand, to keep working on closing the performance gaps in relation to the U.S. where necessary, and on the other hand, it may also mean to deal more directly with U.S. policy itself. Improving the patent system or the conditions for licensing in Europe, for example, may be only half of the story, given the importance of the U.S. markets for European firms. The EU28 may be well advised to take a strong, interest-led position vis-à-vis the U.S. when it comes to discussing improvements of the U.S. patent and licensing environment, as well as to take actions that make European firms fit(ter) for U.S. markets. The U.S. is an important trading partner for technologies, and policy activities should acknowledge and support this. Industry- and market- specific approach to foster knowledge flows and/or licensing: The available data suggests that a differentiated approach is needed when it comes to dealing with fostering the technology-/patent-based knowledge flows, particularly through licensing. In particular, policy might be ill-advised to foster the out- and inlicensing of patents/technology without fine-tuned market- and industry- specific considerations, because firms in different markets may have different needs in this respect. The survey results on licensing show that, for example, the difficulty of identifying the right licensing-partners is for many industries not the prime barrier. Policies that go into the matchmaking of the technology supply with the technology demand side should particularly bear this in mind. Special considerations for New Member States and catching-up economies: The relatively weak position of the New Member State countries has become evident in all three datasets and prompts for thoughts about how to improve this specific situation. The situation is particularly difficult due to an, as one could expect, low awareness of IP issues in the countries, particularly prohibitive costs for international patent protection and also lack of technological capacities, at the current development stage of the economy, of the local industry base to create technological break-through technologies or to absorb such technologies for example through inward technology transfer. A two-fold approach could prove more beneficial: The HCP/HDP data indicates and this is also corroborated by experience in working with the new Member States that every country has so to speak its small gold nuggets in terms of innovators and inventors. 17

20 Identifying these and providing them with targeted support could prove helpful in creating technological nuclei that form the basis of future growth and good performance of the country in the respective fields. The second, more broad approach could be to consider fostering incremental innovation that is small but important improvements over existing technologies, by means of providing cheap, policy-targeted and fast access to IPR protection for such innovations, particularly if they help solve local problems as part of a smart specialisation strategy. Countries such as Japan have a history of fostering inward technology transfer by focusing on improvements over existing main technologies and facilitating such transfer through an adapted patent system. Hence, considerations could be made on how to adapt the national patent systems for this purpose. In Japan, utility models have proved to be an important and successful element of respective policy considerations, so the role of utility models in this context for the New Member States could be also discussed. Methodological considerations: This project has used three sets of quantitative data, and a lot of issues have been described in the individual sections and respective underlying reports that could be the subject of activities to improve the available information base and data on knowledge flows and licensing. We will not go into the respective details here (readers shall be referred to the respective reports), but would like to make the recommendation on a more general level to continue with the improvement of the information base, as an element of evidence-based policy making. With regard to patent/technology licensing, it could be considered to create regular, but scaled down (in terms of the number of questions) surveys on licensing. Apart from these quantitative considerations and against the backdrop of the finding of the need to differentiate highly between individual sectors and markets or to understand licensing as a very individual process that needs to be tailored specifically to the needs of a specific business case -, we would also recommend looking at possibilities to make more use of qualitative research methods, such as focus and expert groups. Even with the best possible industry classifications, it may, for example, not be possible to understand the dynamics of very specific markets and their issues with and use of licensing. Bringing the respective cast of actors for one such market at the table provides opportunities for learning specifics about licensing that would not be possible to obtain with purely quantitative methods (and are hence also a means and input to improve quantitative methods). 2. TECHNOLOGY IN EUROPE 14 COMPARED TO THE REST OF THE WORLD 2.1 Patterns of Science-Technology Linkage In the P&L project, specific attention has been paid to the presence of science when developing technology. In order to obtain relevant indicators reflecting science-technology relations, scientific references cited as prior art in patent documents were matched to articles in the Web of Science. The algorithm developed and refined within the framework of this project succeeds in matching almost 70% of non-patent references (for which sufficient information is available). In absolute figures, for almost 3.3 million non-patent references, a matched article was identified in the Web of Science (NPR s stemming from patents with an application year between 1993 and 2009). Such a result is according to the authors knowledge unprecedented and entails potential for large-scale analysis of science-technology patterns 14 This project ran between 2010 and 2014 while Croatia entered the EU in July Therefore, observations/data for Croatia are not comparable to other EU countries and hence not included in the analysis/report. 18

21 on several levels of analysis. Within the framework of the P&L project, crosscountry citation patterns between (citing) technology (patents) and (cited) science (articles) have been analysed. Main Findings A first question assesses whether supply (of science) and the occurrence of scientific references in patents coincide on a national level. While one observes a slight positive relation between both indicators, it actually becomes clear that distinctive patterns apply for different (sets of) countries. Most European countries display profiles that combine considerable levels of scientific productivity with a considerable presence of scientific references in patent documents. As such, these patterns do not signal the presence of a European paradox in terms of outspoken discrepancies between scientific productivity and the science intensity of technology. For a second set of countries (top left quadrant), higher levels of science intensity (of patents) coincide with lower levels of scientific productivity. The countries in this quadrant are also countries with a lower propensity to patent - both in absolute and relative (per capita) terms. For these countries, the relative share of academic patenting tends to be higher, partly explaining the higher indicator value for science intensity. Supply ( ) versus absorption of science per citing country ( ) Source: KU Leuven Estonia, Croatia, Latvia, Lithuania, Luxembourg, Malta, Romania and Slovakia are not included in the matrix due to low numbers of patents (and/or scientific references) not allowing to reach reliable result. The lines in the graph below signal differential dynamics within emerging versus more developed economies. In a next step, cross-country flows between citing corporate patents and cited science are analysed. This is done by calculating relative citation intensity indices for pairs of citing and cited countries. The results clearly confirm the presence of a significant home bias and albeit weaker proximity effect in science cited in patents. The presence of a home bias varies between countries and technological domains. For several countries traditionally known as technologically strong (DE, FR, UK, ) and especially for the U.S., the home bias is less outspoken. 19

22 The presence of a home bias implies that countries rely on local scientific knowledge when advancing their technological development and innovative activities, hence providing support for the existence of the national innovation systems notion. At the same time, the home bias is not absolute: science-technology citations cross national borders and often occur on a global scale. These findings are in line with a previous study conducted by Veugelers et al. (2012) in which crosscountry citation patterns between corporate (citing) patents and academic (cited) patents were analysed. Finally, the impact of the citation patterns on technological performance of countries has been examined. The results confirm that cross-country citation patterns relate to technological performance on a national level. The presence of foreign citations (stemming from a range of countries) is positively related to technological performance whereby these effects are stronger for countries with a less outspoken scientific and technological profile. 20

23 CITING CORPORATE PATENTS CITED SCIENTIFIC DOCUMENTS NORTH EUROPEAN UNION EFTA REST OF THE WORLD AMERICA AT BE BG CY CZ DE DK ES FI FR GR HU IE IT NL PL PT SE SI UK CH IS LI NO CA US AU BR CN IL IN JP KR NZ RU TW ZA AT 6,83 1,72 1,15 1,12 1,05 1,76 1,15 1,20 1,94 2,21 1,04 1,31 BE 3,62 1,80 1,24 1,27 1,38 1,12 1,34 1,10 1,20 1,00 1,33 1,04 1,48 BG 3,28 3,24 1,10 2,00 1,42 1,48 5,02 1,74 2,19 1,59 1,01 1,11 2,91 1,41 1,09 CY 1,55 1,18 1,25 1,46 1,16 6,63 2,17 2,81 3,02 1,19 3,73 1,93 1,49 1,17 CZ 32,79 1,59 1,58 2,68 2,88 6,71 1,32 2,77 2,14 1,16 1,12 18,21 DE 1,35 1,09 1,01 1,25 1,89 1,00 1,06 1,02 1,01 1,06 1,01 1,14 1,02 1,06 DK 1,18 1,33 1,23 1,47 7,18 1,12 1,30 1,53 1,29 1,40 1,82 1,34 1,07 1,53 1,18 1,11 1,82 ES 6,73 1,02 1,18 1,54 1,22 1,33 1,16 1,08 1,19 1,26 1,20 2,32 1,25 1,56 FI 1,62 9,58 2,10 2,49 1,06 1,35 2,44 1,11 1,09 1,06 1,10 FR 1,10 1,33 1,12 2,33 1,29 1,14 1,02 1,14 1,56 1,65 1,16 1,15 UK 1,39 1,09 1,00 1,04 1,01 1,01 1,88 1,01 1,19 1,68 GR 32,93 4,89 2,66 7,76 1,70 4,58 2,47 2,42 1,93 HU 1,15 1,11 1,18 1,37 21,53 1,10 1,65 1,35 1,21 1,10 1,30 1,87 4,98 IE 1,57 1,89 1,58 1,05 1,32 1,57 12,18 1,20 1,44 1,08 1,15 2,68 1,72 1,11 1,65 1,31 IT 1,05 1,10 1,04 1,16 3,84 1,06 1,22 1,25 1,25 1,32 2,04 NL 1,00 1,52 1,06 1,16 1,04 1,02 2,87 1,49 1,03 1,02 1,18 PL 2,94 1,10 2,56 1,14 25,24 1,55 1,03 1,04 1,71 1,08 1,88 1,63 PT 1,84 3,22 1,50 1,61 34,07 1,02 3,45 8,31 1,26 1,02 2,19 2,62 1,56 SE 1,03 1,08 1,06 1,05 1,22 1,22 1,01 3,84 1,14 1,02 1,50 SI 2,53 1,17 1,27 2,00 1,13 5,18 1,70 100,51 19,38 1,08 4,69 1,26 1,02 5,45 UK 1,39 1,09 1,00 1,04 1,01 1,01 1,88 1,01 1,19 1,68 CH 1,41 1,14 1,07 1,09 1,04 1,14 1,15 1,06 1,10 2,41 1,09 1,04 1,17 1,41 IS 1,12 4,39 3,09 2,23 1,35 1,58 2,24 2,39 1,36 1,61 1,14 1,10 201,37 2,08 LI 1,86 1,15 1,08 3,20 1,69 1,13 16,34 88,79 1,65 1,05 1,87 1,24 1,50 NO 2,29 1,41 1,29 1,12 1,18 1,00 1,10 1,38 1,28 1,17 3,47 1,30 14,32 15,29 1,17 1,02 1,17 1,34 CA 1,07 1,05 1,15 1,01 1,01 1,10 2,57 1,12 1,15 1,43 1,87 US 1,03 1,10 1,28 1,01 1,03 1,08 1,08 1,01 1,04 1,02 1,06 1,14 1,02 1,34 1,06 1,07 1,03 1,05 1,13 1,18 1,01 1,06 1,09 AU 1,18 1,40 1,08 1,04 1,06 1,11 1,02 1,07 1,03 1,15 5,07 1,51 2,08 1,09 BR 1,46 2,38 2,12 1,47 1,45 27,84 1,71 3,05 CN 1,07 1,29 1,02 1,02 1,06 1,90 1,23 1,13 4,24 1,21 1,01 1,20 1,41 1,07 IL 1,13 3,36 1,13 1,22 1,01 1,22 1,01 1,06 4,37 1,41 1,06 1,18 2,65 IN 1,08 1,28 2,60 1,26 1,34 1,11 2,22 1,18 4,69 1,20 1,34 8,28 1,17 1,06 5,40 JP 1,05 1,04 1,07 2,27 1,20 1,03 KR 1,08 1,11 1,05 1,53 1,20 5,22 1,56 NZ 2,23 1,94 1,12 1,39 1,21 1,06 1,05 1,11 1,22 1,19 2,07 1,20 29,57 RU 1,75 1,12 1,02 1,07 1,08 2,02 1,82 1,79 1,09 15,97 TW 1,39 1,44 1,11 1,44 1,06 3,02 1,22 2,32 5,71 ZA 4,05 1,43 1,26 1,76 1,66 2,21 1,75 1,42 1,42 2,07 1,86 1,00 33,24 Source: KU Leuven Overall relative intensities of citation linkages formed by corporate patents (application years EPO, WIPO & USPTO) citing scientific publications produced in period Threshold > 1 and highlighted in green > 1,2 Estonia, Croatia, Latvia, Lithuania, Luxembourg, Malta, Romania and Slovakia are not included in the matrix due to low numbers of patents (and/or scientific references) not allowing to reach reliable results. 21

24 2.2 International collaboration and the role of ERA The evolution of international collaboration as witnessed by means of co-inventor data has been analysed in a final report. Do European countries rely more on international inventor teams when developing technology and if so, is this increase mainly accounted for by cross-border cooperation between EU28 countries? The analysis relies on the inventors' address information listed in EPO patents. We used these data to study the extent and importance of crossborder technological collaborations. More specifically, the frequency of intra-eu collaborations has been assessed and compared with international collaborations located outside EU28 borders. As such, the analysis informs on the effects of the European Research Area's (ERA) creation: how important is the European Research Area in fostering research collaborations, compared to the more general globalisation trend that might characterize cross-country collaboration patterns. Main findings Cross-border collaboration is observed within all world areas. At the same time, there are outspoken differences in the shares of foreign inventions by world region. Asian applicants, with over 95% domestic inventions, are least engaged in international collaboration. For EFTA applicants, almost 50% of patents include a non-domestic inventor. The EU28 and the United States display a similar pattern with approximately 15% of patents including a non-domestic inventor. Shares of domestic, foreign and hybrid inventions by area ( ) Source: KU Leuven Hybrid refers to the share of patents where at least one inventor is from the same country as the applicant, and at least one inventor is from a different country. Shifting the focus to foreign inventions only, we further considered whether or not foreign inventors come from the same world region. EU28 applicants show a clear tendency of collaborating with inventors from within EU28 borders. The observations for the United States are similar to those for the EU28. This tendency to focus collaboration on countries within the world, is opposite to observations in EFTA, where there is a clear and increasing propensity to collaborate with partners outside EFTA. For Asia, collaboration across country borders is very low, but if there is international collaboration in Asian patents, then this is most likely with non-asian inventors. 22

25 Shares of categories for foreign collaboration, by area (aggregated for period ) Source: KU Leuven Hybrid refers to the share of patents where at least one inventor is from the same country as the applicant, and at least one inventor is from a different country. On a more disaggregated level, the results by applicant country (instead of world areas) reveal considerable country differences, especially within the EU28. The domestic share is especially high for countries like Italy, Spain and Germany. This is confirmed in the trend analysis, where these countries are in the quadrant here below, characterised by low to moderate internationalisation and a low growth rate in internationalisation. A much higher share of foreign collaboration is observed for Luxemburg, Ireland, the Netherlands and Belgium; these countries, together with Sweden and Finland also display a considerable growth in terms of internationalisation. 23