1.1 The Growth in Policies to Leverage Public R&D

1.2 Cross-Country Trends in Public R&D

The volume of public sector investment in scientific research is traditionally measured through expenditures on R&D financed by government. R&D expenditures, their distribution across industrial sectors, and the proportion spent on applied and basic R&D are highly variable across countries and have usually evolved with the growth of an economy and the nature of industrial policies to support growth (see also National Science Foundation 2018; UNESCO 2018). The increase in public or private R&D, however, must be seen in the context of the overall growth of R&D. An increase in public R&D is more effective if business expenditures on R&D are high or rising.

Figure 1.1 shows noticeably different overall trends in the R&D intensity (share of R&D expenditures as a percentage of GDP) for high-, middle- and low-income economies between 2000 and 2016. High-income countries spend about 2.5 percent of GDP on R&D, and this is a much larger share than any other group of countries. The sharpest growth in R&D, however, has been in the upper middle-income countries. In both high- and low-income countries, R&D expenditures as a share of GDP have struggled to grow, while lower middle-income countries (which includes countries such as India) have seen a decline in R&D intensity.

Figure 1.1 Share of R&D (measured by GERD) in GDP by income group of countries, 2000–16

Source: UNESCO Institute for Statistics, March 2019

Public sector R&D can be delivered through a variety of institutions. Universities play a big role in high-income economies where industrial capabilities are at the scientific frontier and so benefit from close links to the basic science produced in university science departments. More specialized public research institutes may be preferred in middle- and lower-income countries that have limited resources to invest in scientific infrastructure and that often prefer to concentrate such investments in a few areas of greatest need to the economy and society. In general, as national income per capita decreases, R&D by public research institutes plays an increasingly important role in economic development. As firms in these economies possess low levels of technological capability, they need the help of public R&D to adapt frontier technologies to domestic conditions. Public research institutes generally undertake applied research geared toward the building of prototypes that can be manufactured by local industry.

In high-income economies, the public sector is responsible for anywhere between 20 and 45 percent of annual total R&D expenditure and almost three-quarters of the expenditure on basic research, with the remaining expenditure on private and applied R&D coming from the private sector. On average, government funding is responsible for about 53 percent of total R&D in the middle-income countries for which data are available. Thus, the distribution of R&D between public and private sectors shows that as the level of a country’s per capita income decreases, governmental funding approaches 100 percent.

The data on public and private R&D are patchy but in Figure 1.2 (panels A and B), we plot the data that are available to demonstrate the variability of public R&D even within similar income groups. Although not included in Figure 1.2B, the public sector funded 100 percent of R&D in Burkina Faso in the last year for which data are available (see UNESCO 2018). In Argentina, Bolivia, Brazil, India, Peru, and Romania (also not included in Figure 1.2B) the share of public sector R&D often exceeds 70 percent of total R&D.Footnote ¹⁰

Figure 1.2 Share of public sector in total R&D, high- and middle-income economies A. Share of public sector in total R&D in high-income countries, in percent, 2016 or latest available year B. Share of public sector in total R&D in middle-income economies, in percent, 2016 or latest available year

Importantly, with some exceptions, governments usually provide the majority of the funds for basic research. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view (for the U.S., see the analysis in Reference Arora, Belenzon and PatacconiArora et al. 2015). On average, in 2009, the public sector performed more than three-quarters of all basic research in high-income economies.Footnote ¹¹ These contributions to basic research are becoming more vital as firms focus mostly on product development and as multinational companies in high-income countries scale back their basic research in a number of R&D-intensive sectors (see OECD 2008). In middle-income countries for which data are available (see Figure 1.3), public research is responsible for the majority of basic R&D: close to 100 percent in China, close to 90 percent in Mexico, about 80 percent in the Russian Federation, and about 75 percent in South Africa. A high share of government in basic research may also be an institutional legacy, often seen in former socialist countries.

Figure 1.3 Share of basic research conducted by the public sector for 2017, or latest available year, as a percentage of all national expenditures for basic research

Note: Figure 1.3 provides data from the most recent available years, between 2015 and 2017 for each country. Some of the distinction between higher education institutions – universities and government as well as public research institutes – is simply definitional and depends on what is defined as a university or a public research institute in a given country.*Case study countries

Source: Organisation for Economic Co-operation and Development (OECD), Research and Development Statistics Database, March 2019

1.3 Challenging Factors in Middle-Income Countries

The consistent increase in R&D intensity in upper-middle and middle-income countries – in the latter case much driven by China only – masks several challenges.

Compared to high-income countries, science and technology (S&T) and innovation conditions in middle- and low-income countries face the following challenges:

There are several reasons that explain the limited impact of science on economic development in low- and middle-income countries. First of all, in terms of policy strategies, is the need to address basic economic needs such as poverty and health, with science and technology as second-order priorities. Second-, middle- and low-income countries are a very heterogeneous group, with wide differences in the needs and conditions for knowledge transfer. In most low-income countries, many of the necessary elements for science to have an impact on industrial innovation and society are embryonic, while in middle-income countries the foundations exist but are weakly articulated (WIPO 2011; Reference ZuñigaZuñiga 2011). It is clear, however, that R&D capabilities – both in private and public institutions – in many middle-income economies have improved during the last decade and opportunities to enhance technology commercialization through IP are emerging.

As the remainder of this book shows, structural features have also constrained the development of linkages between universities and firms.Footnote ¹² Often, commercial activity by universities and researchers has been or is still highly regulated or even forbidden. With few exceptions, most universities fully depend on federal budgets and have weak linkages with regional governments and economies. The lack of absorptive capacity in firms and their natural focus on imitative innovation and acquisition of foreign technology as innovation strategies also contribute to fragmentation in national innovation systems (see Reference Navarro, Llisterri, Zuñiga and PagesNavarro et al. 2010). The technological strategies of firms in lower- and middle-income economies often depend on off-the-shelf imported technology, primarily in the form of machinery and turnkey knowledge transfer from abroad. Often these are also the only options for these firms to access current technology.Footnote ¹³ The barriers to industry–science collaboration reported by firms include a lack of communication channels with universities, differences in organizational culture (in respect of timing and product delivery), uncertainty of a market need/demand for research results, and high costs for developing and commercializing university research.Footnote ¹⁴

In this context, one of the main conclusions of this book is that knowledge transfer policies that are not accompanied by policies to strengthen R&D capabilities in firms and industry–science linkages are unlikely to be successful. In addition, as in high-income countries, transforming academia into more entrepreneurial institutions requires cultural change – in particular among researchers, and often an increase in university autonomy, including for more competitive hiring and in terms of resource management. Compared to high-income countries, the following are additional barriers to knowledge exchange from the science base in low- and middle-income countries:

More generally, an additional friction to the development of IP registration and commercialization in many middle- and low-income countries is the sluggish process of patenting at national patent offices and its relatively high cost (see WIPO 2011; Reference ZuñigaZuñiga 2011; Chapters 10 and 11 of this book).

However, these characteristics are not shared equally across all low- and middle-income countries. For the most part, work is ongoing to improve the systemic weaknesses in national innovation systems and to give greater autonomy to universities. As evidenced earlier, many of these countries are also in the midst of implementing or setting up knowledge transfer policies and practices. Indeed, in some cases this has already led to significant impacts, both in terms of measured knowledge transfer and the related broader impacts on public research organizations, firms and the linkages between them.

Finally, this book emphasizes that high-income countries struggle with many of the same challenges when it comes to putting in place functioning knowledge transfer practices. A blueprint that could easily be adopted across institutions and countries therefore does not yet exist, even in high-income economies. Experience and the economic literature show that different stages of development and different innovation systems require different policies and incentives to promote the commercialization of public research (see Reference Guellec, Madies and PragerGuellec et al. 2010). Conditions for knowledge transfer develop over time and depend heavily on research capabilities and science–industry linkages. Having a broad view of the concept of technology commercialization, looking at intermediate steps and broad knowledge transfer activities – not exclusively focused on IP creation and licensing, and academic entrepreneurship – makes for good policy advice.

1.4 Rationale for the Selection of Country Cases

The heterogeneity of high- and middle-income countries with regard to basic features about the organization of public R&D suggests that simply instituting relevant laws and regulations is only a first ingredient to stimulating industry–science linkages. A number of conditions need to be in place at the country and institutional level to reap the resulting benefits. Moreover, diverse stages of development will require different approaches and complementary policies, including safeguards for avoiding the downside risks of university patenting.

Our approach in this book has been to explore in detail the interaction between the institutional frameworks, public policy constraints, and adoption of third mission policies in six countries with a view to distilling lessons from their experience. Although heterogeneous in themselves, this group of countries consists of three high-income economies (the United Kingdom, Germany, and the Republic of Korea) whose experience of third mission policies differs from the oft-cited example of the U.S. Germany is an exemplar of the institution of a collective market economy where public sector R&D and the state more generally played a large role in economic growth and technological prowess. The United Kingdom, by contrast, is more similar to the U.S. in relying on liberal market institutions to promote third mission policies. The Republic of Korea (like Germany) relied on a strong public research institute sector for technological catch-up but has found the institutional change needed to implement broad-based growth (moving away from reliance on large chaebol companies) hard to achieve. We also include the study of three large middle-income countries, namely Brazil, China, and South Africa. Brazil inherited institutions that are similar to those in continental Europe, but it was also influenced by the US system. China transformed itself from a public research institute-led system to one in which universities were reformed to engage with domestic industry. South Africa managed both radical political change and reformed its university system and linkages to industry.

Figures 1.4 and 1.5 build on Figures 1.1–1.3 for the six countries that we study in this book. They enable us to see the precise nature of differences between countries that may often belong to the same income group classification.

Figure 1.4 R&D intensity (GERD as a percentage of GDP), case study countries

Source: Authors based on data from the UNESCO Institute for Statistics and OECD

Figure 1.5 Share of GERD financed by the government, case study countries

Source: Authors based on data from the UNESCO Institute for Statistics and OECD

Figure 1.4 on R&D intensity shows that the Republic of Korea, China, and Germany saw rising shares of R&D in their economies. The United Kingdom (despite being in the high-income group) had R&D shares that were almost a whole percentage point lower than those in Germany and lower than China (a middle-income country). The R&D intensity was stagnant in the United Kingdom and similar trends are observed for South Africa and Brazil. Figure 1.5 shows the percentage of gross expenditure on R&D that was financed by the government. All countries, except for South Africa, show a declining trend. China and the Republic of Korea show some of the lowest levels, while Brazil and South Africa have markedly higher levels of government-financed R&D. Thus, the six case studies confirm that the backdrop against which stronger knowledge exchange policies have been pursued has been a declining share of government-financed R&D in the context of an overall decline in R&D intensities.

As we noted in Section 1.3, the distribution of R&D between the public and private sectors has varied considerably between countries even within a particular group. In general, countries that had a large role for the state also ended up having large public R&D shares. Figure 1.2 also reports the distribution of public and private sector R&D, including for the group of case study countries, except Brazil, for which no data were available. The smallest shares of public R&D were for the Republic of Korea and China, which have seen a strong role of the state and also public research institutes in their growth histories. South Africa has the largest share of public R&D followed by Germany and the United Kingdom.

A last indicator worth looking at is the distribution of basic research between the share of universities and the share of the government sector in the six countries we study (see Figure 1.3). Here we find that in China most basic research (over 55 percent) happens in the government and the university sector (here institutions of higher education), whereas this is much lower at about 30 percent for the Republic of Korea and Germany and even lower, at 20 percent, for South Africa and the United Kingdom. Basic research in universities is smallest for the United Kingdom and highest for China.

1.5 Summary and Plan of the Book

The most appropriate frameworks for spurring the commercialization of publicly funded inventions – whether in public research institutes or publicly funded universities – depends on the institutional context and will vary due to different starting points. Yet, for the most part, a “one glove fits all” approach has dominated policy thinking in this area. At times, policymakers and institutions have been overreliant on the filing of IP as the only instrument to enhance the impact of science on the economy. A more realistic assessment of the state of their research and innovation systems to identify the role that IP can play in such development (given both its opportunities and costs) is missing and sorely needed.

Providing a more nuanced understanding of optimal policies for knowledge transfer is the overriding objective of this book. Such policies need to be grounded both in the historical evolution of university–industry relations and systematic data underpinned by a rigorous conceptual understanding of what is involved in knowledge exchange between university and industry. In keeping with this objective, we use a recursive approach in the book, as follows.

Part I develops an understanding of broad institutional differences in the nature of public science across countries; a conceptual framework for thinking about knowledge exchange, knowledge transfer metrics, and survey and evaluation frameworks; and a standardized method to assess national or institutional strategies. This first chapter sets out the rationale for third mission activities, shows how policies for third mission activities developed in a fiscal situation that saw a decline in funding for public R&D in many countries, and sets out the main institutional differences between high- and middle-income countries. It also compares basic trends in public R&D for the six countries studied. Chapter 2 develops a conceptual framework to guide the evaluation of knowledge transfer policies, practices and outcomes. Chapter 3 then looks at what corresponding patent metrics exist to produce (internationally) comparable data on formal knowledge transfer practices.

Part II (Chapters 4–9) recounts the historical evolution of knowledge exchange policy and outcomes in three high-income countries (the United Kingdom, Germany, and the Republic of Korea) and three middle-income countries (China, Brazil, and South Africa). Country authors use the unique history of their country to produce narratives of policy evolution and the reasons for success or failure of intended outcomes.

Part III uses an inductive approach to distill optimal policies and identify optimum metrics to support a better framework for knowledge exchange. An important differentiating feature of this book is that we recognize that knowledge exchange is a two-way process where the ability of firms to absorb university-generated knowledge is as important as the ability of the university to reach out. Thus, Chapters 10 and 11 outline policies to raise industrial involvement and university involvement in knowledge exchange, respectively. In each chapter, we contrast the experience of high-income and middle- income countries to draw out the policy implications. What knowledge transfer laws and practices have been put in place in high- and middle-income countries? How have new policies to support IP licensing affected other knowledge transfer channels? Which approaches have demonstrated the best outcomes for public institutions and for firms but also at the broader macro-level? Do approaches exist that are particularly relevant to developing countries? Chapter 12 concludes the book by discussing the interplay between the objectives of knowledge exchange policy and the metrics available to evaluate these objectives, and what remains to be done in this regard. Which overall economic and other impacts have been measured and how? What additional data are required to provide a comprehensive set of metrics for use in benchmarking, monitoring, and policy evaluation? What are the possible sources of such data?

Trend toward Actively Motivating and Empowering Researchers to Participate in Knowledge Transfer

The direct involvement of academic researchers has proved to be a determinant in the success of knowledge transfer. This clearly calls for cultivating a culture that supports and encourages both invention disclosures and the participation of inventors in the transfer process. There is also a need for a better understanding of the strategies of the various types of inventor/researcher involved and their motivations to participate in the process.

To boost academic entrepreneurial activity, universities, and public research institutes are introducing an ever wider range of incentives for researchers, where IP and commercialization efforts receive greater rewards comparable to publications. Among the specific incentives are: generous royalty and equity terms; tying IP generation and research commercialization to career development; sufficient time to engage in IP-related activities (leave of absence, course reductions, relief of admin responsibilities, etc.); research funding and infrastructure; internal commercialization support and mentoring; entrepreneurship education programs; recognition through awards and public acknowledgement, etc. Empirical findings seem to suggest that the influence of such incentives (both monetary and nonmonetary) is not always predictable, given the differences in motives, perspectives and cultures of the academic scientists.

Questions that merit further empirical investigation include:

• What drives academics to be engaged in the commercialization of their research outcomes?

• Which factors can have an impact on the attractiveness of academic incentives (such as differences between the researchers in terms of gender, age, research field, characteristics of the ecosystem in which they operate, the seniority of researchers)? Is it therefore possible that a variety of incentives may be required for different types of researchers?

• What is the effect of the royalty share allocated to researchers? A large share can potentially enhance technology licensing, whereas a lower share is more likely to boost spinoffs; at the same time, too low a share allocated to the institution may not be sufficient to cover overall costs and may challenge the quality of services their knowledge transfer offices (KTO) provide.

• Is giving inventors a share of the equity in a spinoff rather than a simple share of returns a more effective way to motivate, considering the higher levels of uncertainty over returns, but also the prospect of higher returns than might accrue to licensing?

• What kind of remuneration packages are necessary to attract highly skilled employees at the KTO, noting that internal policies may prevent institutes from providing competitive salaries?

• How can the efficiency of institutes’ support services be improved (e.g., by creation of an association of KTOs to pool support efforts)?

• How does the existence of competing incentives affect the engendering of an entrepreneurship culture, considering that researchers tend to have multiple “principals” (mainly the university itself, heads of departments, KTO, research council, government and external agencies) who often incentivize different outputs?

To evaluate the effectiveness of their incentives program, institutes must also establish comprehensive and systematic performance indicators, including some specific to IP-based commercialization. Empirically grounded metrics are critically important to an effective incentive structure. An important caveat is that incentive structures tend to be too focused on the supply side, which is the ability of the university to transfer knowledge. Attention needs also to be paid to the demand side, which involves the demand from industry for assistance in resolving problems and the region’s ability to absorb the research results.

Trend toward Socially Responsible Research Commercialization

It would appear that entrepreneurial institutions around the world face more pressure to be responsive to the needs of society and environmental issues. The growing concern of this social dimension of higher education calls for resolute efforts to devise strategies that will establish them as drivers of societal well-being, while identifying the right indicators to monitor socioeconomic benefit flowing from such engagement. Successful cases prove that institutes have the means at their disposal to integrate a social dimension in their knowledge transfer practices (including those that are IPR-based), such as creating research programs directed to solving social and environmental problems; anticipating which technologies may have applications that address important unmet social needs; adopting socially responsible licensing provisions that increase the availability of medicines and environmental technologies in developing countries; retaining the right to grant additional licenses to manufacturers of generic drugs; negotiating licensing terms that allow third parties to access and distribute the innovation and its derivative products; promoting the creation of spinoffs; participation in community-based research; etc. In addition to those, the chapter presents a set of levers for preventing the potentially negative impacts of IPR-based knowledge transfer.

While policymakers and institutes tend to collect and employ mostly quantitative performance indicators to capture scientific productivity and commercial outcomes, the local/regional impact of universities and public research institutes extends far beyond knowledge transfer and tangible outputs (in terms of human capital attraction, formation of entrepreneurship capital within a locale, informal networks, new ideas, etc.). However, as the chapter indicates, establishing clear causal relationships between IPR-based knowledge transfer and these societal benefits is hard. Accordingly, statistics on the number of licenses issued or the number of spinoffs established do not effectively do justice to answering the question of how institutions address tangible socioeconomic outcomes.

Despite the fact that there is a trend afoot in some high-income countries to assess the success of knowledge transfer using alternative criteria, such as social impact or contribution to welfare, there is still no consensus on a set of systematic social impact measurements.

Concluding Observations

There is no magic formula for harnessing public research for innovation, given that different factors and levels of support interventions affect knowledge transfer outcomes. At the same time, there are magical “elements” or “factors” that the success stories have in common. The chapter does a nice job in elucidating such success factors at the country and institutional level. It is, however, important to note that success is a result of more collaborative efforts within an innovation ecosystem. For example, Yale’s success in creating the biotech cluster is to a large extent due to the fact that it implemented changes in collaboration with other players in the region, to push for local economic impact. Countries also need to put into practice initiatives that promote and strengthen academia and business collaborations. One example in Brazil is the ITec platform, which was financed by the Ministry of Science & Technology and counts on the participation of companies and universities to feed the framework of demands and offers.

At the micro/institutional level, two efforts deserve closer attention and empirical investigation, namely, getting appropriate incentive structures and the commitment to socially responsible commercialization.

The transformation of institutes to become more entrepreneurial may be supported by creating new incentives and performance-linked criteria for researchers. How academic incentives work, and how they can be used to achieve intended results, remains a contested issue. In the university and public research institute contexts, the pursuit of science and innovation driven by external incentives, especially financial rewards, is considered by some as going against the traditional values of academia. However, international experience shows that institutionalizing an efficient incentive program is a critical precondition for increasing opportunities for commercializing university inventions. The challenge for institutes lies in selecting the types of incentive and their associated metrics, based on the institute’s mission, culture, and goals, and the country’s innovation ecosystem.

Socially responsible entrepreneurship is in large part a cultural attribute. Institutes can do their bit to encourage its development by, for instance, formulating policies that promote ethically acceptable and socially desirable knowledge transfer coupled with appropriate performance indicators. It would appear that institutes still struggle with (1) defining what “socially responsible” means and (2) measuring the extent to which their socially responsible policies and practices have meaningful impact. Maintaining a system of comprehensive indicators, including variables that can also measure social impact, is crucial for any country, regardless of its level of development, to help institutes better evaluate their roles in the creation of regional innovation and social value through research commercialization.

2.1 Introduction

As outlined in Chapter 1, a common policy goal in both high- and middle-income countries is to increase the commercialization of research findings produced by the public research sector in order to support economic growth. This process involves the transfer of knowledge produced by public research organizations, including both universities and public research institutes, to private sector businesses or government agencies.

A diverse range of policies have been implemented in many countries to encourage knowledge transfer, including the establishment of knowledge transfer offices (KTOs)Footnote ¹ at universities and public research institutes. Other policies include support for open publication and close collaboration between universities/public research institutes and businesses. One important issue is how to evaluate the success of these policies in terms of their economic impacts and their effect on various actors within an innovation system. Possible evaluation methods include case studies and the collection and analysis of knowledge transfer metrics. The latter have often involved the use of IP licensing data.

IP licensing is only one of several channels for transferring knowledge produced by universities and public research institutes to private firms. However, it is an important focus for research on knowledge transfer, both in the research reported in this book and in the academic literature. The research focus on IP licensing partly reflects its importance to knowledge transfer policies in developed countries, as described in Chapter 1, and partly reflects the widespread availability of relevant data, in contrast to a lack of data for other knowledge transfer channels.

This chapter provides a conceptual framework for the country case studies included in this book, and identifies the most commonly used metrics for knowledge transfer mediated by the licensing of IP. It describes different methods of knowledge transfer, policies and practices for supporting knowledge transfer (particularly via IP), and the costs and benefits of IP licensing. One key message from the chapter is that reliance on IP metrics may underestimate the extent of knowledge transfer in the economy and that informal methods of transfer may be a precursor to more formal relationships.

2.2 Channels of Knowledge Transfer

The public research sector has three main roles that are supported by government policy. The first is to create trained and educated citizens, the second is to push the frontiers of knowledge by undertaking cutting edge research, and the third is to support economic activity through several channels for transferring knowledge from universities and public research institutes to the business sector (see Figure 2.1). In recent years this third role of knowledge transfer is becoming increasingly important and is often referred to as the “third mission” of universities. Economically useful knowledge can also be transferred to government and nonprofit organizations. The transfer of knowledge to government often occurs through the procurement of research services, with the goal of improving public services or addressing social needs.

Figure 2.1 Knowledge transfer channels between the public research sector and businesses

Source: WIPO (2011)

Knowledge transfer occurs through both informal and formal channels. Informal channels include reading the literature, attending conferences, hiring trained graduates,Footnote ² and discussions via personal contacts. These have also been combined under the rubric of “open science” because they make knowledge publicly available at little or no cost (Reference Cohen, Nelson and WalshCohen et al. 2002). Formal channels include licensing, collaboration and research agreements, and contracting-out. In general, informal channels do not require the recipient of the knowledge to make a payment to the institution via a contract, whereas formal channels use a contract to mediate payment. Knowledge can be transferred entirely through informal channels, entirely through formal channels, or through a combination of both, for instance, when informal discussions lead to a research agreement that results in an IP license.

It is important to place knowledge flows from public research to firms in context. They play only a minor role in the flow of knowledge within an innovation system. A 2010 survey of manufacturing firms in the United States of America (U.S.) found that 49 percent of firms obtained the invention behind their most important innovation from external sources, attesting to the importance of knowledge flows to an innovation system, but only 10 percent of them reported that this invention was from a university. Importantly, however, inventions obtained from technology specialists, including universities, were of higher value than inventions obtained from other sources such as customers or suppliers and 37 percent of inventions obtained from technology specialists were based on a formal channel (Reference Arora, Cohen and WalshArora et al. 2016).Footnote ³

A consistent issue, identified in multiple studies, is the dependence of knowledge transfer on the ability of firms, particularly firms in the region where the university or public research institute is located, to absorb or use inventions produced by public research. Research shows that knowledge transfer activities increase with the technological capabilities of domestic or regional firms (Reference Van Looy, Landoni, Callaert, Van Pottelsberghe, Sapsalis and DebackereVan Looy et al. 2011; Reference Curi, Daraio and LlerenaCuri et al. 2012; Reference Hewitt-DundasHewitt-Dundas 2012; Reference Calderón-Martínez and García-QuevedoCalderón-Martínez and García-Quevedo 2013; Reference Okamuro and NishimuraOkamuro and Nishimura 2013; Reference Hussain, Rahman, Zainol and YaakubHussain et al. 2014; Reference Ranga, Temel, Ar, Yesilay and SukanRanga et al. 2016). This is an important issue in low- and middle-income countries and for regional institutions in developed countries, where firms may lack sufficient absorptive capacity (see Chapter 10). In addition to regional differences, firms that rely on informal personal contacts are smaller and have lower levels of absorptive capacity than firms that use formal knowledge transfer methods (Reference Freitas, Geuna and RossiFreitas et al. 2013).

Not surprisingly, firm involvement in knowledge transfer from public research organizations increases with the firm’s R&D intensity (Reference Freitas, Geuna and RossiFreitas et al. 2013; Reference Okamuro and NishimuraOkamuro and Nishimura 2013; Reference Kafouros, Wang, Piperopoulos and ZhangKafouros et al. 2015). One study also finds that firm involvement with universities increases with the number of universities in a region, possibly because it increases the probability of a good match between the needs of firms and what universities can offer, or because greater competition between universities increases the flexibility of academic and KTO staff (Reference Okamuro and NishimuraOkamuro and Nishimura 2013).

Reference Albuquerque, Suzigan, Kruss and LeeAlbuquerque et al. (2015) describe an international survey on the use of different knowledge channels by firms in low and middle-income countries in Africa, Asia and Latin America. In two low-income countries (Nigeria and Uganda) informal methods dominate (Reference Kruss, Adeoti, Nabudere, Albuquerque, Suzigan, Kruss and LeeKruss et al. 2015), whereas in middle-income countries in Asia (China, Malaysia, and Thailand) the most common methods are consultancy and research contracts (Reference Schiller, Lee, Albuquerque, Suzigan, Kruss and LeeSchiller and Lee 2015). One explanation for the importance of contractual relationships in Asia is their usefulness in building innovative capacity and problem-solving abilities in firms. In four middle-income Latin American countries (Argentina, Brazil, Costa Rica, and Mexico), both contracts and informal channels are used more frequently than IP-mediated methods (Reference Dutrénit, Arza, Albuquerque, Suzigan, Kruss and LeeDutrénit and Arza 2015).

From a public policy perspective, providing information to businesses at no cost via informal channels will be beneficial if it increases the number of businesses that use the information to develop commercial products and processes. In addition, competition will reduce costs for consumers. The exception is when no business will invest in commercializing knowledge without an exclusive license, for instance, when the cost of commercializing knowledge is high but the cost for competitors to copy it is low. In this case, public research institutes and universities need to be able to provide firms with exclusive licenses to IP-protected knowledge.

One of the main purposes of the 1980 Bayh-Dole Act in the U.S. was to allow public research organization to provide exclusive licenses. The Act also led to widespread adoption of the “IP licensing model” for knowledge transfer, defined in this book as the use of IP to mediate the transfer of knowledge. The IP licensing model has been widely used, even when IP is not required for firms to develop and commercialize knowledge, as when an exclusive license is not given. This is partly because universities and public research institutes were attracted by the potential income from both nonexclusive and exclusive licenses, as well as the need to recover the costs of maintaining a KTO. In addition, the IP licensing model can have other benefits, such as signaling the existence of inventions to firms.

Importantly, policies or research that account for only one type of linkage can provide only a partial understanding of the patterns of interaction between the public research sector and businesses. Nevertheless, the focus of this conceptual framework is on knowledge transfer systems that involve, at some point, formal transfer methods, while recognizing that many formal methods will originate in informal relationships between university researchers and private businesses.

2.3 The Role of Policies and Practices in Promoting Knowledge Transfer

Policies to support knowledge transfer between public research institutes and universities and firms should be designed to support multiple knowledge channels and should take into consideration the advantages and disadvantages of each channel and the suitability of different types of knowledge for specific channels. The role of KTOs has adapted over time to take these issues into account, with a greater recognition of the need for KTOs to support informal channels (for instance by arranging “meet and greet” events between academics and business), in addition to their traditional role in supporting the IP licensing model.

Universities and public research institutes can also create a supportive environment for knowledge transfer through secondary activities such as educational programs to teach entrepreneurship to students and faculty and by creating innovation incubators and science parks (Reference Rothaermel, Agung and JiangRothaermel et al. 2007). Incubators and science parks can attract businesses to conduct some of their activities close to the university and encourage contacts with researchers and entrepreneurial students and staff.

Relevant policies and practices to support knowledge transfer occur at both the national and institutional level.

A review of existing policy research to date reveals a few important lessons (WIPO 2011). First, despite the general trend toward institutional ownership and commercialization of university/public research institute inventions, a diversity of legal and policy approaches persists in terms both of how legislation is anchored in broader innovation policy and of the specific rules on the scope of patenting, invention disclosure, incentives for researchers (such as royalty sharing), and whether safeguards are instituted to counteract the potentially negative effects of patenting. Second, there is a large variation in the means of implementing such legislation, as well as the available complementary policies to enhance the impact of public R&D and to promote academic entrepreneurship.

2.4 Costs and Benefits of the IP Licensing Model

Since knowledge transfer can occur through multiple channels, an important policy goal is to ensure that the IP licensing model will drive knowledge transfer and business innovation while at the same time preserving open science (Reference Foray, Lissoni, Hall and RosenbergForay and Lissoni 2010) and the benefits of other contractual or informal channels for knowledge transfer (Reference Rosli and RossiRosli and Rossi 2014; Reference VeugelersVeugelers 2016).Footnote ⁴ Combining informal and formal channels can have a positive effect on innovation outcomes (Reference Siegel, Waldman and LinkSiegel et al. 2003; Reference Link, Siegel and BozemanLink et al. 2007; Reference Grimpe and HussingerGrimpe and Hussinger 2013). The use of both channels could be especially important to spinoffs (Reference HayerHayer 2016).

Maintaining multiple channels and supporting positive synergies among them depends on maximizing the advantages and minimizing the disadvantages of existing and potential policy approaches. Effective outcomes also depend on the specific details of IP policy implementation at the national, regional and institutional levels.

The potential costs and benefits of the IP licensing model, as discussed in the literature, are summarized in Tables 2.1 and 2.2. Table 2.1 distinguishes between possible benefits and costs for the two respective main agents (firms and public research organizations), while Table 2.2 summarizes the systemic impacts of IP licensing on science, the economy, and society. Table 2.3 adds additional notes of relevance to middle-income countries (WIPO 2011; Reference ZuñigaZuñiga 2011).

2.5 Measuring Knowledge Transfer

Table 2.4 lists the variety of possible knowledge channels, including informal channels consisting of “open science” and two types of formal channel. There is a lack of consistency in the literature on the definition of formal channels, with some studies combining consultancy and contract research with informal methods in order to focus on the difference between IP-mediated channels and all other channels (Reference Tartari and BreschiTartari and Breschi 2012; Reference Abreu and GrenevichAbreu and Grenevich 2013). The formal channels are divided into those that support the creation of new knowledge by a university or public research institutes, and contractual methods for accessing existing knowledge produced by a university/public research institute via IP licensing. Table 2.4 also identifies the main data sources on knowledge transfer for each channel.

With a few exceptions,Footnote ¹⁰ surveys show that the most common channels for both firms and academics in high-income countries are open science, followed by contracts for the creation of new knowledge (Reference Cohen, Nelson and WalshCohen et al. 2002; Reference Siegel, Waldman and LinkSiegel et al. 2003; Reference Cosh, Hughes and LesterCosh et al. 2006; Reference D’Este and PatelD’Este and Patel 2007; Reference De Fuentes and DutrénitDe Fuentes and Dutrénit 2012; Reference Hughes and KitsonHughes and Kitson 2012; Reference Grimpe and HussingerGrimpe and Hussinger 2013; Reference Freitas, Geuna and RossiFreitas et al. 2013; Reference Okamuro and NishimuraOkamuro and Nishimura 2013; Reference Dutrénit, Arza, Albuquerque, Suzigan, Kruss and LeeDutrénit and Arza 2015; Reference Kafouros, Wang, Piperopoulos and ZhangKafouros et al. 2015; Reference Kruss, Adeoti, Nabudere, Albuquerque, Suzigan, Kruss and LeeKruss et al. 2015; Reference Schiller, Lee, Albuquerque, Suzigan, Kruss and LeeSchiller and Lee 2015). This is also partly reflected in the source of knowledge transfer income. In the United Kingdom, contract and collaborative research account for the majority of university income from knowledge transfer, with IP income accounting for only 2 percent to 4 percent of the total (Reference Cosh, Hughes and LesterCosh et al. 2006; Reference Zhang, MacKenzie, Jones-Evans and HugginsZhang et al. 2016).

2.6 Collecting Knowledge Transfer Metrics for Licensing

The most common source of basic metrics is surveys of KTOs on activities that are part of the IP licensing model. These metrics are available on an intermittent or annual basis in many high-income countries, including the member states of the European Union, the U.S., Canada, Australia, and New Zealand. Most surveys of KTOs follow the definitions and standards set by the AUTM for its surveys of KTOs in the U.S. and Canada. The AUTM has been collecting metrics since the early 1990s.

Table 2.5 summarizes seven basic metrics from KTO surveys: the number of (1) invention disclosures, (2) patent applications, (3) patent grants, (4) research agreements, (5) license agreements, (6) startup establishments, and (7) total license revenue earned. None of these metrics is a direct measure of commercialization. Invention disclosures refer to an unknown potential for commercialization, with many never patented or licensed. Patent grants can remain unlicensed, research agreements can result in no new knowledge of commercial value, and licenses may never lead to commercialized processes or products.

None of these seven metrics measures the successful commercialization of IP produced in universities and public research institutes – all are metrics of inputs into potential commercialization.Footnote ¹¹ The first three metrics (invention disclosures, patent applications, and patent grants) are the furthest from commercialization, but invention disclosures are the first step in an IP-mediated commercialization process. The next step, if an evaluation of the invention disclosure results in a decision that there is commercial potential, is to file a patent application or seek other forms of IP if a patent is not suitable.

The two metrics that are closest to commercialization are the number of startups established and license revenue earned. Licenses indicate that a firm has an interest in commercializing the licensed IP, but many licenses, particularly for generic technologies or research tools, fail to lead to the commercialization of new goods, services or processes.Footnote ¹² Similarly, research agreements and startups indicate that a firm is interested in the commercial potential of knowledge produced by institutions, but we do not know if the research agreement produced useful research results or if the startup was able to commercialize a product or process.

Identifying the commercialization of new knowledge produced by universities and public research institutes requires the ability to identify licenses that earn running royalties (royalties earned on and tied to the sales of products) or the ability to follow startups over time and determine if they commercialized IP obtained from the institution. Recent AUTM surveys have collected data on running royalties (AUTM 2015b) and “net product sales,” which includes sales from IP licensed to startups.Footnote ¹³ European KTOs have begun to track outcomes for startups, but there is not yet agreement on the types of outcome that should be collected over time (Reference Arundel, Barjak, Es-Sakdi, Barjak, Perrett, Samuel and LilischkisArundel et al. 2013).

2.7 Conclusions

This chapter describes the different channels that are used by universities, public research institutes, and firms to transfer knowledge between them and the role of policies and institutional practices in supporting knowledge transfer. The chapter largely focuses on the IP licensing model, due to extensive academic research on this channel and data availability, but it is essential for a full understanding of knowledge transfer to also evaluate the role of informal and contractual knowledge transfer channels, as summarized in Table 2.4. Several of the country studies in this book take a more holistic perspective by evaluating the role of each channel and how these channels have changed over time in response to policy changes or economic development.

The collection of metrics on knowledge transfer via licensing is essential for benchmarking, identifying the factors that support or hinder knowledge transfer, and to inform policy. There are seven basic metrics that all countries should collect on the IP licensing model, plus supplementary metrics of relevance to specific policy issues, such as if licensing is benefiting domestic firms or the efficiency of IP use, as measured by the share of IP that is licensed. Additional metrics that would support a holistic perspective on knowledge transfer are discussed in Chapter 12.

Background

This chapter provides an excellent overview of how to think about evaluating public sector knowledge transfer activities. It provides both a conceptual framework for doing so, as well as potential metrics. And it also includes a nice review of the now large body of economic and policy literature on these topics that has been developed over the past two decades.

Overall, the conceptual framework seems complete. Unlike much previous work in this area, it emphasizes that firms benefit from academic research not only through what the authors call formal channels (patenting and licensing) but also through more informal channels, often associated with so-called open science. And that there may be tensions, as well as complementarities, between the two channels.

Here, I offer a few additional thoughts on the conceptual framework and the indicators, and also on public policy and evaluation going forward.

Conceptual Framework

As I mentioned, the conceptual framework seems fairly comprehensive. There are, however, three things that I think are missing from the potential benefits side of the equation.

First, while the authors mentioned financial benefits, it is important to emphasize that these revenues are not “profits” for public research organizations, but rather are typically used to fund future research. That is, the potential benefit is more funding for science and technology, which may be particularly important in resource-constrained environments. I am not necessarily endorsing this rationale: as the authors point out, the financial benefits for many organizations may be small, and there are costs as well, But I think it is an important one to keep in mind since it is often a major part of the justification for formal involvement in knowledge transfer activities.

A second motivation for knowledge transfer organizations, and taking out patents and licenses in particular, is to create a way to incentivize inventor involvement and commercialization. I did not see much about this in the chapter. This may be particularly important in countries and contexts where academic involvement has previously been limited or where there are strong cultural norms militating against it. And it is most important for “embryonic” inventions needing further development, where the inventor possesses specialized tacit knowledge. However, in this context, it should be emphasized, at least in countries where inventors rather than the public research organizations previously held title to patents, that it is unclear that shifting toward ownership by the research organizations increases inventor incentives, and could in fact blunt them. Reference HvideHvide and Jones’s (2018) paper in the American Economic Review provides one example. Specifically, in Norway, university researchers used to have rights to their own inventions, under the so-called “professor’s privilege.” After this was changed to be more like the US model, in which universities took rights, entrepreneurship and patenting rates by academic researchers decreased. But in general, the conceptual framework might also consider the effects of these organizations, and of patent rights, on incentivizing inventor involvement in the commercialization process.

Third, another potential benefit public sector ownership is the ability to harness this ownership to influence downstream outcomes, such as prices, access, or availability. This is mentioned in passing in the discussion of patents and access to medicines in developing countries. But it might be brought into the conceptual framework as well. That said, as far as I know, this potential role for public sector ownership has been used only sparingly.

Another observation is less about the conceptual framework than about its application. I would like to see more recognition in academic knowledge and knowledge transfer in general about what is one of the most robust empirical findings from economics over the past half-century: Patents matter more for research incentives in some fields than others. In particular, in drugs and chemical-based industries, patents are more important for appropriating returns from R&D than in other sectors (Reference Cohen, Nelson and WalshCohen et al. 2000). Although there is no direct evidence on this in the context of university or public sector knowledge transfer (at least as far as I can recall), it would stand to reason that patents (and the prospect of exclusive licenses) are more important as commercialization incentives in some fields than others. (Drugs and biotech inventions seem like the strongest case.) In some industries, academic patents and KTOs might simply get in the way of transfer or commercialization (although they may help achieve other objectives noted here and in the chapter, such as financial returns or upstream control of particular technologies). I suspect that the costs and benefits of different channels of knowledge and knowledge transfer presented in the conceptual framework will vary sharply by field, a fact that should be considered in its application.

Indicators

The list of indicators provided is quite comprehensive. One thing I will add is that at least some of these indicators could be manipulated. For example, it is possible for an organization to increase invention disclosures and patent applications without really increasing the underlying construct of interest, namely, the extent of knowledge or knowledge transfer. Often the policy discussion ends up focusing on the indicator rather than the underlying construct. The fact that there are multiple indicators, not all of which are so easily manipulable, does help here.

But this leads to my second point, one that the authors acknowledge but is important enough to restate. It is much easier to measure the more formal activities than to measure the informal ones. If one accepts that the informal ones are important (maybe even more important based on the qualitative and historical analyses cited in the chapter), this presents a big problem. Specifically, it is possible that KTO activities could be nominally increasing some of the formal indicators but having a detrimental effect on knowledge transfer using informal channels. But evaluators are not really seeing it since we cannot measure the latter well. Even worse, and this is a theme emphasized in personnel economics, if performance is multidimensional but we only have good performance measures for some dimensions and reward based on those, this could distort incentives (for organizations, researchers) toward the better measurable but less important dimensions. I am not sure what to do about this – perhaps better bibliometric measures of more informal contributions would help (see, e.g., Reference Bryan, Ozcan and SampatBryan et al. 2019) – but policymakers, in particular, should keep this in mind. Mission statements acknowledging that traditional channels of knowledge dissemination are also important to the organization may also be helpful in setting norms.

Beyond Benchmarking: Better Evidence for Policy

Let’s step back a bit. The academic knowledge transfer movement started to accelerate in the United States of America in the 1970s and was codified by the Bayh-Dole Act of 1980. I and others have argued that Bayh-Dole was passed based on questionable evidence that the lack of patents and exclusive licenses on academic research had previously limited social returns from public research in any serious way, with the possible exception of some pharmaceuticals requiring significant investment in clinical trials (Reference EisenbergEisenberg 1996; Reference Mowery, Nelson, Sampat and ZiedonisMowery et al. 2004). Bayh-Dole ignored technology and knowledge transfer through the informal channels, and differences across fields in the importance of patents. And the specific indicators measuring how well the formal channels were (or were not) working were problematic (Reference EisenbergEisenberg 1996). Similarly, other countries emulating Bayh-Dole have drawn largely on aggregate evidence of patenting and licensing (and perhaps revenues and startups) to make the case that this policy was a success, with a lack of attention to (a) the extent to which these indicators actually capture knowledge transfer and (b) potential negative effects on informal channels (Reference Mowery and SampatMowery and Sampat 2004).

This has been an active debate for several decades, and need not be rehashed here. However, to avoid having this same debate again several decades from now, it might be useful to implement new KTOs and patent policies in a way that facilitates evaluation going forward. That is, drawing on the conceptual framework presented in this chapter, it would be useful to prespecify outcomes and indicators of interest (including effects on formal and informal knowledge transfer), and to be clear about what would constitute evidence that the policies and institutions are working or not. Since prepost analyses can be hard to interpret, some experimentation may also help, for example rolling out policies across institutions or regions or campuses in a way that facilitates quasi-experimental evaluation. The questions raised in this chapter about what works, and potential tradeoffs, are hard ones, and in addition to collecting better indicators, policymakers might implement new laws in a way that helps us learn from new experiences in a more structured way than was possible with Bayh-Dole and its early counterparts in other OECD countries. This approach will also force organizations to be transparent and precise about the objectives they hope to achieve.

One type of experimentation that might be particularly fruitful is on licensing practices. As this chapter points out, to the extent that the goals of KTO activities are more than simply financial, patents and exclusive licenses are really only needed only for a subset of research outputs. Codifying this idea in KTO policies and missions, and making better efforts to gauge the need for an exclusive license, could also be useful (Reference Ayres and OuelletteAyres and Ouellette 2016). Building a rebuttable presumption of low-cost non-exclusive licensing into KTO patent policies and practices might be one way to do this. It may work better in some fields and countries than others, but could also create an additional layer of bureaucracy that impedes knowledge transfer, or be subject to gaming. It is quite hard to know theoretically. This is exactly why more experimentation – with a commitment to later evaluation, based on prespecified indicators and hypotheses, drawing on the framework presented in this chapter – could be extremely valuable.

3.1 Introduction

Policymakers increasingly seek to bolster the effectiveness of academic research in fostering innovation. Universities and public research institutes are encouraged to engage with industry partners and spur knowledge transfer from academia to the private sector.

One way of facilitating this knowledge transfer is by patenting research outputs from universities and public research institutes. Patents, plus close engagement between universities and public research institutes and the private sector, are two important factors that make university–industry knowledge transfer successful (Reference Perkmann, Tartari and McKelveyPerkmann et al. 2013).

Collaboration between academic organizations and the private sector is not new. Universities and public research institutes played important roles in propelling developments in agriculture, aviation, and the chemical and pharmaceutical sectors as early as the nineteenth century (Reference Mowery, Nelson, Sampat and ZiedonisMowery et al. 2004; Reference Rosenberg and SteinmuellerRosenberg and Steinmueller 2013; WIPO 2015). Academic patenting has also been used by university researchers since the late 1800s (Reference Mercelis, Galvez-Behar and GuagniniMercelis et al. 2017).

Since the late 1970s, many countries have changed their legislation and created support mechanisms to encourage interaction between universities and firms, including through knowledge transfer (Reference Graff, Krattiger, Mahoney, Nelsen, Thomson, Bennett, Satyanarayana, Graff, Fernandez and KowalskiGraff 2007). In 1980, the United States of America (U.S.) passed the Bayh-Dole Act, landmark legislation which allowed for patenting of research outputs funded by the government. Many European countries followed suit about a decade later (Reference Wright, Clarysse, Mustar and LockettWright et al. 2008; Van Reference Looy, Landoni, Callaert, Pottelsberghe, Sapsalis and DebackereLooy et al. 2011). A direct effect of this type of policy is a rise in academic patenting and licensing activities in universities and public research institutes across the U.S. and in certain European countries.

Policies that encourage patent protection of government-funded research work are intended to promote the commercialization of university inventions, with the aim of facilitating innovation-led economic growth (Reference So, Sampat and RaiSo et al. 2008). As a by-product, this type of policy provides an avenue for generating income for universities (Reference GeunaGeuna 2001) and tracking patenting by research organizations has become one way of measuring their performance.

This chapter focuses on how to identify patenting activities by universities and public research institutes so as to develop cross-country comparison of academic patenting activities. In particular, it proposes a harmonized approach to capture patent filings for these public research organizations across different countries using patent data filed through the Patent Cooperation Treaty (PCT) as well as national-level patent data compiled using the PATSTAT database.

Using patent filing data from the PCT and PATSTAT, we present a new data set of universities and public research institutes which allows for better insights into how effective university knowledge transfer mechanisms have been, and will potentially help to analyze their research performance. Our objective is to gauge the patenting outputs of these organizations, allowing us to measure the evolution of patenting activity over time, benchmark the performance of public research organizations, and enable cross-country comparisons.

This chapter is organized as follows. The next section focuses on academic patenting, in particular, what the data tell us as well as their limitations, and discusses how academic patenting may or may not have changed the norm of universities and public research institutes. We present our methodology for capturing the patenting activities of universities and public research institutes in the third section. The penultimate section analyzes the results of our work by showcasing the results from using the PCT and PATSTAT databases through cross-country and cross-technology comparisons. The last section concludes with direction for future research.

3.2 Why Focus on Patenting in Academia?

Total patent filings at the national level are often used as an indicator of the innovativeness of a certain country. By the same logic, patent filing activities can measure the innovativeness of a university or public research institute. But this is not the complete story.

The availability of patent data from the US Patent and Trademark Office (USPTO) and the European Patent Office (EPO) has contributed to a rise in quantitative analyses of academic patenting (Reference Rothaermel, Agung and JiangRothaermel et al. 2007). University and public research institutes patent filing activities have been used by decision makers to assess the effectiveness of their knowledge transfer offices (KTOs), whether research projects are close to the technological frontier and inventive, the performance of their research staff, and so on. But it is important to remember that this metric is an imperfect measure of innovativeness.

3.3 How to Measure Academic Patenting?

3.4 Who Is Patenting and Where?

Academic patenting – measured by patent filing activities by universities and public research institutes worldwide – is on the rise. Since 1995, the number of PCT applications filed by universities and public research institutes has been steadily increasing (see Figure 3.1). The growth in PCT applications filed by university and public research institute applicants combined since 1995 can be divided into two periods. In the period 1995–2008, the average annual growth rate in academic patent filings was 13.3 percent. The period 2009–16 saw average annual growth of 2.4 percent in PCT applications, 2.3 percent in university applications and – 0.4 percent in public research institute applications. Growth in public research institute and university PCT filings declined during and after the economic downturn of 2009 compared to the previous period of high growth.

Figure 3.1 Public research institute and university PCT applications, absolute numbers (left) and as a percentage of total PCT applications (right), 1995–2016

Source: WIPO Statistics Database, July 2017

Patent filings captured by PATSTAT data also show an increase in academic patenting. Figure 3.2 shows the total number of patent families created by universities and public research institutes. In 2014, about 162,000 patent families were created by university and public research institute applicants worldwide. On average, the total number of university and public research institute patent families (16.5 percent) grew much faster than overall total number of patent families (4.9 percent) over the period from 1995 to 2014. As a result, the share of university and public research institute patent families in total families has been increasing rapidly – especially for universities – reaching 11.4 percent in 2014, up from 1.5 percent in 1995.

Figure 3.2 Trend and share in university and public research institute patent families worldwide, 1995–2014

Sources: WIPO Statistics Database and EPO PATSTAT database, July 2017

Again, the trend in university and public research institute patent families can be divided into two distinct periods. The period 1995–2004 saw average annual growth of 12.1 percent, with patent families from universities (12.3 percent) and public research institutes (11.8 percent) growing at almost same pace. The period 2005–14 saw even faster growth. The average annual growth rate for this period was 5.4 percent for all families and 19.6 percent for university and public research institute families combined. However, patent families from universities (22.4 percent) grew much more quickly than those from public research institutes (11.3 percent).

The slowdown of the growth in PCT patent filings and the increasingly rapid growth of PATSTAT patent filings seem to contradict one another. However, this is not necessarily the case.

The share of foreign-oriented patent filings by academic organizations has been decreasing. Figure 3.3 shows the number of foreign-oriented patent families created by universities and public research institutes from 1995 to 2013 and the share of foreign-oriented patent families in total patent families for each type of applicant.

Figure 3.3 Trend in university and public research institute foreign-oriented patent families worldwide and share of total, 1995–2013

Sources: WIPO Statistics Database and EPO PATSTAT database, July 2017

Figure 3.3 shows that while the number of foreign-oriented patent families from universities and public research institutes increased steadily over the past two decades, their respective shares of total patenting activity by those organizations decreased sharply. In 1995, universities created 2,058 foreign-oriented patent families and public research institutes created 1,177. In 2013, universities and public research institutes created three to four times more foreign-oriented patent families – 5,858 and 4,702, respectively. The combined total of foreign-oriented patent families for universities and public research institutes increased each year between 1998 and 2013 to reach 10,560 in 2013.By way of contrast, the share for universities decreased from 39.5 percent of foreign-oriented patent families in 1995 to 4.8 percent in 2013. This indicates that the number of patent families that have no international dimension is increasing much more rapidly than the number of foreign-oriented patent families.

What explains the drop in foreign-oriented patent filings by universities and public research institutes? That is outside the scope of this chapter. It may indicate a change in academic patenting strategy, with more universities and public research institutes preferring to file in one office rather than several. But it could also be due to the filing strategy of academic organizations of one country: China.

3.4.1 By Income Level

In the PCT data, European and US universities and public research institutes have traditionally accounted for the bulk of academic filings globally.Footnote ¹¹ These high-income countries accounted for the vast majority of university (87 percent) and public research institute (80 percent) PCT filings in 2016. US universities accounted for 38 percent of all PCT applications filed by universities in 2016, about 11 percentage points below their 2007 share (Figure 3.4). In the same year, the shares of the top five public research institute origins in total public research institute filings ranged from 19 percent for France to 9 percent for Germany.Footnote ¹²

Figure 3.4 Share of university and public research institute PCT filings for top ten origins in 2007 and 2016

Source: WIPO Statistics Database, July 2017Note: PCT data are based on the publication date and first-named applicant. Universities include all types of educational organizations, and public research institutes include private nonprofit organizations and hospitals.

However, Asian academic organizations, led by China, have been catching up quickly over the past few decades. The top five origins of university PCT filings in 2016 were the U.S. (4,050), China (1,169), the Republic of Korea (1,139), Japan (985), and the United Kingdom (446). In contrast, the top five origins of university PCT filings in 2007 were the U.S., Japan, France, the United Kingdom, and Germany. The change in top five origin ranking between 2007 and 2016 can be explained by the sharp rise in PCT patent filings from China and the Republic of Korea – by 9 and 7 percentage points, respectively.

Shares for the middle-income group increased rapidly between 2007 and 2016, by 10 percentage points for universities and by 13 percentage points for public research institutes (see Figure 3.5). Chinese universities accounted for 83 percent of total middle-income university filings in 2016, while Chinese public research institutes represented 72 percent of total middle-income public research institute filings. The other main middle-income origins in 2016 for universities were South Africa (forty-seven applications), Turkey (thirty-six), India (thirty-three), Malaysia (thirty-two), Colombia (twenty-eight), Brazil (twenty-five), Mexico (nineteen), and Morocco (eighteen); and for public research institutes they were India (132), Malaysia (fifty), South Africa (twelve), Turkey (eleven), and Brazil (nine).

Figure 3.5 University and public research institute PCT filings originating from middle-income countries as a share of total university and public research institute PCT filings

Source: WIPO Statistics Database, July 2017Note: PCT data are based on the publication date and first-named applicant. Universities include all types of educational organization, and public research institutes include private nonprofit organizations and hospitals.

Comparing Academic Patenting in High- and Middle-Income Economies

Figure 3.6a shows the share of university and public research institute PCT applications in the total number of PCT applications by income group. The shares for high-income countries grew consistently during the period 1980–2015, and ranged from 5.1 percent to 8.5 percent. In the period 1980–90, university and public research institute PCT applications from middle-income economies represented just over 2.8 percent of those countries’ PCT applications. That share increased dramatically to over 8.0 percent in 1991–2000, and was fairly stable at almost 9.1 percent during the period 2001–15. PCT filings from China could potentially bias the middle-income share due to the high filing activity in that country. However, if China is removed from the count, the share of university and public research institute PCT applications in the total number of PCT applications from middle-income countries actually increases to 10 percent. This shows that universities and public research institutes play an important role in the innovation capability of a number of middle-income economies.

Figure 3.6 Increase in university and public research institute filings by high- and middle-income groups3.6a Share of university and public research institute filings in total PCT applications by income group (%), 1980–20153.6b Share of university and public research institute applications in total patent applications by income group (%), 1980–2013

Source: WIPO Statistics Database, April 2016

Figure 3.6b depicts the share of university and public research institute patent applications in total patent applications by income group. For high-income economies, this share increased gradually from about 1.2 percent to 5.1 percent between 1980–90 and 2011–13. Most of this increase originated from universities. The share of university and public research institute applications in middle-income countries has exceeded that of high-income economies since 1980, and increased sharply from about 5.0 percent in 1980–90 to nearly 18.9 percent in 2011–13.

Figure 3.7 decomposes the patenting activity for the most active countries over the last decade. As Figure 3.7a shows, in 2004–13 China accounted for slightly less than half (49.0 percent) of all patent applications filed by universities across the world. It was followed by the U.S. (14.0 percent), and the Republic of Korea (11.3 percent). These top three countries combined accounted for nearly three-quarters (74.3 percent) of the filings originating from the world’s universities.

Figure 3.7 University and public research institute patenting by leading origin countries3.7a University patent applications in the world for selected countries (%), 2004–133.7b Public research institute patent applications in the world for selected countries (%), 2004–13

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

Filings from public research institutes are less concentrated than those from universities, as shown in Figure 3.7b. Public research institute filings from China (31.2 percent), the Republic of Korea (23.3 percent), and France (12.5 percent) combined accounted for 67.0 percent of total filings – 7.3 percentage points below the combined share for the top three countries in university filings (74.3 percent). These shares also reflect a shift in university and public research institute filings from the U.S. and Europe toward Asia.

Figure 3.8 shows the trend over the past decade in PCT filings for selected origins. The key findings can be summarized as follows: US university PCT filings represented about 7.6 percent of total US PCT filings between 2006 and 2015. The number of US university PCT filings remained relatively stable throughout this period, varying between a minimum of 3,560 in 2010 and a maximum of 4,573 in 2014. US public research institute PCT filings accounted for slightly more than 1 percent of total US PCT filings over the past decade and amounted to 753 PCT filings in 2015.

3.8a Public research institute and university PCT applications from high-income countries, absolute numbers, 2006–15

3.8b Share of public research institute and university PCT applications from high-income countries, country shares (%), 2006–15

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

Figure 3.8 The trend over the past decade in PCT filings for selected origins

The shares of university and public research institute PCT filings from Germany were also quite stable, each representing between 2 percent and 3 percent of total PCT filings from Germany between 2006 and 2015. For universities, the total number of PCT filings in 2015 was 490 and for public research institutes 456.

PCT filings by French universities accounted for between 3.2 and 7.5 percent of the country’s PCT filings since 2006. The number of PCT filings from French universities increased from 204 in 2006 to 671 in 2015. The share of French public research institute PCT filings was nearly 12 percent in most of the reported years, and their number of PCT filings increased from 646 in 2006 to 1,165 in 2015.

As for the share of university PCT filings from the United Kingdom, it tended to increase slightly over time and accounted for 10.3 percent of total UK PCT filings in 2015. The number of PCT filings reached 545 in 2015. The share of UK public research institute PCT filings was 1.1 percent in 2015 and represented only fifty-seven PCT filings.

The share of Japanese university and public research institute PCT filings tended to decrease over time. The share for universities decreased from 5.3 percent in 2006 to 3.1 percent in 2015, while that for public research institutes fell from 2.6 percent to 1.1 percent. These declines are due to a fall in the number of PCT filings originating from Japanese universities and public research institutes; in 2015 they filed 1,346 and 480 PCT filings, respectively.

The share of filings from universities in the Republic of Korea’s PCT filings increased markedly during the period, from 5.1 percent in 2006 to 9.4 percent in 2015, with the number of PCT filings rising from 306 in 2006 to 1,364 in 2015. In contrast, public research institute PCT filings decreased as a share of the total from 7.8 percent in 2006 to 3.6 percent in 2015, mainly because the overall number of PCT filings from the Republic of Korea increased faster than the number of public research institute filings.

Figure 3.9 shows data for a selection of middle-income countries. Indian public research institutes accounted for 9.6 percent of total Indian PCT filings in 2015, with 135 filings. The share for universities peaked at 6.5 percent in 2010, having increased regularly over the previous decade. It stood at 1.8 percent in 2006 and 3.4 percent in 2015, with fifteen and forty-eight PCT filings, respectively.

3.9a Public research institute and university PCT applications from middle-income countries, absolute numbers, 2006–15

3.9b Share of public research institute and university PCT applications from middle-income countries, country shares (%), 2006–15

Source: WIPO Statistics Database, April 2016

Figure 3.9 University and public research institute PCT filings in middle-income countries

The share of university PCT filings from South Africa has increased markedly over the past decade, from 5.4 percent in 2006 to 18.1 percent in 2015. This reflects an increase in the number of PCT filings from twenty-three in 2006 to fifty-six in 2015. In contrast, the shares and numbers of PCT filings from South African public research institutes have remained stable since 2006; in 2015 South African public research institutes filed ten PCT applications, representing 3.2 percent of the country’s PCT filings.

As for Mexico, the numbers and shares of public research institute and university PCT filings remained small for the whole period. Mexican public research institutes and universities filed on average about twenty-five PCT applications per year each in most of the years reported, accounting for a maximum of 15.8 percent of total PCT filings from Mexico.

The share of public research institute PCT filings from Malaysia has increased sharply since 2006 and accounted for nearly half of total PCT filings originating from the country (44.0 percent). The number of public research institute filings increased from two in 2006 to ninety-two in 2015. Likewise, the share of university PCT filings increased, from 5.0 percent in 2006 to 14.5 percent in 2015, with numbers up from just three filings in 2006 to thirty-nine in 2015. University and public research institute PCT filings combined accounted for two-thirds (65.0 percent) of PCT filings from Malaysia in 2014.

Chinese university and public research institute PCT filings remained quite stable as a share of total Chinese PCT filings between 2006 and 2015, with on average 4.7 percent and 3.1 percent respectively throughout this period. However, the numbers of PCT filings were six to eight times higher in 2015 than a decade earlier. The number of PCT filings for universities increased from 183 in 2006 to 1,547 in 2015, and from ninety-six to 607 for public research institutes.

According to PATSTAT data, university and public research institute patent filings in France increased by an average annual growth rate of 4.7 percent between 2004 and 2013, reaching 4,810 applications (Figures 3.10a and 3.10b). In Japan, the number of university and public research institute applications stood at 7,264 in 2010, but declined to 5,100 in 2013. In the Republic of Korea, 22,441 university and public research institute applications were filed in 2012, and the average annual growth rates is 15.9 percent in 2004–13.

3.10a Public research institute and university patent applications from high-income countries, absolute numbers, 2004–13

3.10b Public research institute and university patent applications from high-income countries, country shares (%), 2004–13

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

Figure 3.10 University and public research institute patent filings using PATSTAT data

Patents filed by US universities and public research institutes amounted to about 11,000 and 14,000 per year in the period 2004–13, with a decline to around 12,000 in 2005–10. US universities have been patenting their innovations for many years but because of the number of patents filed by the private sector, the university share stood at about 3.9 percent of total filings in 2013.

In China, university and public research institute patent applications combined grew from 8,740 in 2004 to 111,397 in 2013, with an average annual growth rate of 33.2 percent since 2004 (Figures 3.11a and 3.11b). Chinese university patenting since 2004 shows a sharp increase in filing, making some Chinese universities among the most active in the world in terms of patent-filing activity. This can be explained in part by government grants to research institutes and universities that file a large number of patent applications, and related initiatives.

Figure 3.11 University and public research institute patent filings for middle-income countries3.11a Public research institute and university patent applications from middle-income countries, absolute numbers, 2004–133.11b Public research institute and university patent applications from middle-income countries, country shares (%), 2004–13

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

Two main points emerge from the above comparisons. First, the share of university and public research institute patent filings in all applications in high-income countries is more stable than their share in the middle-income group. Second, in terms of numbers of filings, middle-income countries are more heterogeneous than high-income countries.

Figure 3.12 shows the share of patent applications from universities and public research institutes in selected countries. The countries with the highest share of university filings in their total filings are China (14.8 percent), Malaysia (12.8 percent), Spain (6.9 percent), Israel (6.0 percent), Brazil (4.2 percent), and the Republic of Korea (4.2 percent). The countries with the highest share of public research institute applications are India (9.1 percent), France (5.7 percent), China (3.6 percent), the Republic of Korea (3.6 percent), and Spain (3.4 percent).

Figure 3.12 University and public research institute patent applications as a share of total applications for selected countries (%), 1980–2013

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

The shares of university and public research institute patent filings tend to be higher in the middle-income group than in the high-income group. It therefore seems particularly appropriate to encourage knowledge transfer in certain middle-income countries such as Brazil, China, India, Malaysia, and South Africa.

3.4.2 By Technology Field

Overall, university and public research institute patenting activity primarily concerns biomedical and pharmaceutical inventions, broadly defined.Footnote ¹³ This is true of high-income countries and other economies alike. It is not surprising, as these industries are the most science-driven. However, whether patenting in these technological fields is demand- or supply-driven is less clear.

On the basis of PCT data, it can be shown that for the period 2007–16, university filings mainly occurred in the chemistry sector (51 percent) (Figure 3.13), followed by instruments (24 percent), and electrical engineering (17 percent). The three sectors combined accounted for 92 percent of PCT applications filed by universities.

Figure 3.13 Distribution of PCT applications by technology sector, 2007–16

Source: WIPO Statistics Database, July 2016

Public research institutes also made their largest share of PCT applications in the chemistry sector (44 percent). Their share of PCT applications filed in the electrical engineering sector was relatively high, at 28 percent of their total filings. Together with instruments (20 percent), the top three sectors for public research institutes accounted for 92 percent of their total filings, precisely like the cumulative share of the top three sectors for universities.

In 2016, universities filed the largest number of PCT applications in the fields of pharmaceuticals (15 percent), biotechnology (13 percent), and medical technology (10 percent) (see Figure 3.14). These were also the top three fields for public research institutes. For public research institutes, pharmaceuticals accounted for 12 percent of total PCT filings, as did biotechnology. Medical technology represented 8 percent of public research institutes’ total PCT filings.

Figure 3.14 Share of PCT applications for the top three fields of technology, 2016

Source: WIPO Statistics Database, July 2016

Table 3.2 shows the share of patent applications filed worldwide by universities and public research institutes for selected technology fields in 2013–15, based on data from the PATSTAT database. Of the thirty-five technology fields, university applicants filed 40 percent of their applications in their top five fields: biotechnology (14.9 percent), pharmaceuticals (8.5 percent), measurement (5.7 percent), materials, metallurgy (5.5 percent), and organic fine chemistry (5.3 percent).

Table 3.2 Share of patent applications filed in selected technology fields by applicant type, 2013–15

Technology field	Applicant type	Patent filings	As a share of all university/public research institute filings (%)
Biotechnology	University	184,175	14.9
Pharmaceuticals	University	183,509	8.5
Measurement	University	143,493	5.7
Materials, metallurgy	University	80,614	5.4
Organic fine chemistry	University	114,147	5.3
Chemical engineering	University	72,235	4.3
Basic materials chemistry	University	66,177	3.6
Other special machines	University	58,141	2.8
Computer technology	University	91,807	2.7
Electrical machinery, apparatus, energy	University	77,325	2.3
Biotechnology	Public research institute	64,110	5.2
Measurement	Public research institute	62,151	2.5
Pharmaceuticals	Public research institute	48,923	2.3
Materials, metallurgy	Public research institute	31,605	2.1
Chemical engineering	Public research institute	33,323	2.0
Organic fine chemistry	Public research institute	42,277	2.0
Basic materials chemistry	Public research institute	31,323	1.7
Other special machines	Public research institute	28,653	1.4
Computer technology	Public research institute	37,728	1.1
Electrical machinery, apparatus, energy	Public research institute	33,861	1.0

Sources: WIPO Statistics Database and EPO PATSTAT database, April 2016

Patent applications filed by public research institutes were not as concentrated among their top five (14.1 percent) as universities. These top five fields were: biotechnology (5.2 percent), measurement (2.5 percent), pharmaceuticals (2.3 percent), materials, metallurgy (2.1 percent), and chemical engineering (2 percent).

As described already, universities and public research institutes largely concentrate their filings – patent filings as well as PCT filings – in science-based technology fields, especially in pharmaceuticals and the biological fields.

The five universities that filed the largest number of PCT applications in 2016 were all in the U.S. (Figure 3.15). They mainly filed in pharmaceuticals and biotechnology. Pharmaceuticals accounted for the largest share of PCT filings by Johns Hopkins University and the University of Texas System, while biotechnology was the main field of technology for Harvard University, MIT, and the University of California.

Figure 3.15 Shares of leading technology sectors in PCT applications filed by the top five universities

Source: WIPO Statistics Database, April 2017Note: ASTAR is the Agency of Science, Technology and Research, CEA is the Commissariat à I’Énergie Atomique et aux Énergies Alternatives, INSERM is the Institut National de la Santé et de la Recherche Médicale, MIT is the Massachusetts Institute of Technology. Public research organizations include private organizations and hospitals. For confidentially reasons, data are based on publication date. WIPO’s IPC technology concordance (available at: www.wipo.int/ipstats) was used to convert IPC symbols into thirty-five corresponding fields of technology.

The top five public research institutes in PCT filings were more diversified. Only ASTAR and INSERM had two of their three main technology fields belonging to the chemistry sector. China Academy of Telecommunication Technology filed the bulk of its applications in digital communications. The Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA) and the Fraunhofer-Gesellschaft had each of their three main fields of technology within the electrical engineering sector.

Figure 3.16 shows the share of the top three fields of technology for selected universities and public research institutes in their total patent families created worldwide in 2010–13. All selected universities and public research institutes created a quarter or more of their patent families in their top three fields of technology. Precisely half the patent families created by the Korea Electronics Telecomm belonged to digital communications, telecommunications, and computer technology. The CEA is also highly concentrated in its top three fields of technology (electrical machinery, measurements, and semiconductors) as these three fields accounted for 41.3 percent of its total families.

Figure 3.16 Top three technology fields for selected universities and public research institutes, 2010–13

Source: WIPO Statistics Database and EPO PATSTAT database, October 2016

Among this selection of ten universities and public research institutes, eight had measurement and six electrical machinery among their top three fields of technology. Pharmaceuticals and biotechnology – which are two popular fields of technology among universities and public research institutes – appears only among the top three fields of one public research institute (CNRS) and one university (Tokyo University). This is due to the selection of universities and public research institutes, which shows that large organizations can be specialized in quite different fields of technologies.

3.4.3 By University

The University of California was the largest user of the PCT System in 2016, with 434 published PCT applications (Table 3.3). It has maintained that position since 1993. Massachusetts Institute of Technology (236) ranked second, followed by Harvard University (162), Johns Hopkins University (158), and the University of Texas System (152). Seven of the top ten universities were located in the U.S.; Seoul National University of the Republic of Korea (122) – in sixth position – was the highest-ranking non-US university, while Japan’s University of Tokyo (108) ranked seventh. While the top ten was dominated by U.S.-based organizations, the top twenty list comprised ten US and ten Asian universities. China’s Shenzhen University was in joint thirteenth position with eighty-seven published PCT applications, making it the highest-ranking Chinese university for PCT filings.

Table 3.3 Top PCT applicants for the university sector in 2016

Overall rank	Change in position applicant’s name from 2015	Origin	Published applications	Change from 2015
35	15 UNIVERSITY OF CALIFORNIA	USA	434	73
83	8 MASSACHUSETTS INSTITUTE OF TECHNOLOGY	USA	236	23
119	10 HARVARD UNIVERISTY	USA	162	4
125	11 JOHNS HOPKINS UNIVERSITY	USA	158	−12
133	12 UNIVERSITY OF TEXAS	USA	152	−11
172	63 SEOUL NATIONAL UNIVERSTY	Republic of Korea	122	27
198	25 UNIVERSITY OF TOKYO	Japan	108	7
207	22 LEUAND STANFORD JUNIOR UNIVERSTY	USA	104	5
220	118 HANYANG UNIVERSITY	Republic of Korea	101	33
232	23 UNIVERSITY OF FLORIDA	USA	97	−11
235	62 UNIVERSITY OF PENNSYLVANIA	USA	96	20
243	57 UNIVERSITY OF MICHIGAN	USA	94	−22
262	42 KOREA UNIVERSITY	Republic of Korea	87	12
262	480 SHENZHEN UNIVERSITY	China	87	58
262	120 KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY	Republic of Korea	87	30
270	50 TSINGHUA UNIVERSITY	China	84	−18
270	228 CHINA UNIVERSITY OF MINING AND TECHNOLOGY	China	84	41
307	3 CALIFORNIA INSTITUTE OF TECHNOLOGY	USA	73	−1
314	222 KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY	Saudi Arabia	72	32
314	17 KYOTO UNIVERSITY	Japan	72	−4
321	421 NAGOYA UNIVERSITY	Japan	69	40
329	181 NORTHWESTERN UNIVERSITY	USA	67	25
329	43 COLUMBIA UNIVERSITY	USA	67	−13
342	20 OSAKA UNIVERSITY	Japan	65	−7
343	6 NANYANG TECHNOLOGICAL UNIVERSITY	Singapore	64	1
350	70 DUKE UNIVERSITY	USA	62	10
350	40 DANMARKS TEKNISKE UNIVERSITY	Denmark	62	−12
361	116 UNIVERSITY OF NORTH CAROLINA	USA	60	15
361	137 ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE	Switzerland	60	17
396	64 YONSE UNIVERSITY	Republic of Korea	56	−14
396	14 KYUSHU UNIVERSITY	Japan	56	−1
396	6 TOHOKU UNIVERSITY	Japan	56	0
411	127 PEKING UNIVERSITY	China	54	−27
420	173 UNIVERSITY OF COLORADO	USA	52	15
435	9 SOUTH CHINA UNIVERSITY OF TECHNOLOGY	China	50	1
435	45 UNIVERSITY OF WASHINGTON	USA	50	−6
449	49 UNIVERSITY OF PITTSBURGH	USA	49	6
459	166 ISIS INNOVATION LIMITED	UK	48	−30
468	155 UNIVERSITY OF MARYLAND	USA	47	12
468	375 INDIANA UNIVERSITY	USA	47	22
482	111 KYUNGPOOK NATIONAL UNIVERSTY	Republic of Korea	46	9
486	151 NATIONAL UNIVERSTY OF SINGAPORE	Singapore	45	−24
486	50 UNIVERSITY OF ARIZONA	USA	45	5
495	64 STATE UNIVERSITY OF NEW YORK	USA	44	−7
495	15 YALE UNIVERSITY	USA	44	2
518	222 CORNELL UNIVERSITY	USA	42	−35
530	6 IMPERIAL INNOVATIONS LTD.	UK	41	1
546	n/a UMM AL-QURA UNIVERSITY	Saudi Arabia	40	36
561	51 YEDA RESEARCH AND DEVELOPMENT CO. LTD.	Israel	39	−3
571	49 UNIVERSITY OF HOUSTON	USA	38	−3
571	106 UNIVERSITY OF ILLINOIS	USA	38	6

Source: WIPO Statistics Database, April 2017Note: The university sector includes all types of educational organizations. For confidentiality reasons, data are based on publication date.

For the sixth consecutive year, the CEA of France was the top PCT applicant among public research institutes, with 329 published PCT applications (Table 3.4). It was followed by the Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung of Germany (252) and the Agency of Science, Technology and Research of Singapore (162).

Table 3.4 Top PCT applicants among governments and public research institutes in 2016

Overall rank	Change in position applicant’s name from 2015	Origin	Published applications	Change from 2015
52	9 COMMISSARIAT A L’ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNAT1VES	France	329	−80
81	22 FRAUNHOFER-GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV.	Germany	252	−71
119	23 AGENCY OF SCIENCE TECHNOLOGY AND RESEARCH	Singapore	162	14
143	12 INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INSERM)	France	146	9
146	33 CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLCGY	India	145	27
156	1 CENTRE NATIONAL DE LA RECHERCE SCIENTIFIQUE (CNRS)	France	135	−2
172	22 NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY	Japan	122	10
194	7 COUNCIL OF SCIENTIFIC AND INDUSTRIAL RESEARCH	India	109	−1
273	56 KOREA INSTITUTE OF INDUSTRAL TECHNOLOGY	Republic of Korea	83	12
307	83 SLOAN-KETTTERNG INSTITUTE FOR CANCER RESEARCH	USA	73	17
324	50 CONSEO SUPERIOR DE INVESTIGACIONES CIENTFICAS (CSC)	Spain	68	9
403	64 MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH	USA	55	−12
449	5 BATTELLE MEMORIAL INSTTUTE	USA	49	0
459	283 RIKEN (THE INSTITUTE OF PHYSICAL AND CHEMICAL RESEARCH)	Japan	48	19
486	309 MIMOS BERHAD	Malaysia	45	−76
495	27 KOREA ELECTRONICS TECHNOLOGY INSTITUTE	Republic of Korea	44	3
495	128 COMMONWEALTH SCIENTIFIC AND INDUSTRIAL RESEARCH ORGANISATION	Australia	44	9
518	173 NEDERLANDSE ORGANISATIE VOOR TOEGEPAST NATUURWETENSCHAPPELIJK ONDERZOEK (TNO)	Netherlands	42	−22
518	252 MAX-PLANCK GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN E.V	Germany	42	14
561	162 KOREA RESEARCH INSTITUTE OF SCIENCE AND BIOTECHNOLOGY	Republic of Korea	39	−16
571	84 ELECTRONICS & TELECOMMUNICATIONS RESEARCH INSTITUTE OF KOREA	Republic of Korea	38	5
571	197 JAPAN SCIENCE AND TECHNOLOGY AGENCY	Japan	38	−21
630	523 UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY	USA	35	17
669	484 KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY	Republic of Korea	33	15
688	251 KOREA INSTITUTE OF ENERGY RESEARCH	Republic of Korea	32	−18
708	94 DALIAN INSTITUTE OF CHEMICAL PHYSICS CHINESE ACADEMY OF SCIENCES	China	31	−5
708	445 SHENZHEN INSTITUTE OF ADVANCED TECHNOLOGY	China	31	13
727	43 CEDARS-SINAI MEDICAL CENTER	USA	30	2
727	32 CLEVELAND CLINIC FOUNDATION	USA	30	−1
752	341 INSTITUTE AUTOMATION, CHINESE ACADEMY OF SCIENCES	China	29	10
752	213 KOREA INSTITUTE OF MACHINERY & MATERIALS	Republic of Korea	29	7
752	36 KOREA RESEARCH INSTITUTE OF STANDARDS AND SCIENCE	Republic of Korea	29	−1

Source: WIPO Statistics Database, April 2017

Note: The government and public research institutes sector includes private nonprofit organizations and hospitals. For confidentiality reasons, data are based on publication date.

Figure 3.17 shows the distribution of PCT applications for the top thirty origins, broken down by four types of applicant: businesses, individuals, universities, and government and research organizations. In 2016, 85.5 percent of all PCT applications belonged to business applicants, 7.5 percent to individuals, 5 percent to universities and 2 percent to public research institutes. Among the top thirty origins, universities accounted for a large share of applications in Morocco (42.9 percent), Colombia (33.7 percent), South Africa (16.2 percent), and Malaysia (14.4 percent). These five origins all belong to the middle-income category. They were followed by applicants from Singapore (14 percent), Spain (13.6 percent), Israel (9 percent), Australia (8.9 percent), and the United Kingdom (8.6 percent). In contrast, several countries – including Egypt, the Philippines and Sweden – had no PCT applications filed by universities in 2016.

Figure 3.17 The share of the business sector in total PCT applications from selected origins

Note: The government and public research organizations (PROs) sector includes private nonprofit organizations and hospitals. The university sector includes all educational institutions. For confidentiality reasons, data are based on the publication date.

Source: WIPO Statistics Database, April 2017

Public research institutes represented a high share of applications originating in Malaysia (22.5 percent), Singapore (17.8 percent), the Philippines (11.8 percent), India (9.5 percent), and France (9.3 percent). Eleven of the top thirty origins had no PCT filing activity from public research institutes in 2016. For Colombia (33.7 percent), Malaysia (36.9 percent), and Morocco (42.9 percent), university and public research institute PCT filings combined accounted for more than one-third of their total PCT filings.

With 34,352 patent families worldwide, Panasonic of Japan was the top PCT applicant for the period 2010–13 (Table 3.5). It was followed by two other Japanese companies: Canon (29,036) and Toyota Jidosha (26,844). The top 100 list mainly comprises multinational companies. However, eleven Chinese universities and one Korean university as well as one Korean public research institute feature among the top 100 applicants. Combined, these thirteen applicants accounted for 8 percent of all patent families held by the top 100 applicants.

Table 3.5 Top fifty-five patent applicants worldwide, based on total number of patent families, 2010–13

Applicant	Origin	2010	2011	2012	2013	Total number of patent families, 2010–13
Panasonic Corporation	Japan	10,780	10,284	8,295	4,993	34,352
Canon Inc.	Japan	6,686	7,132	7,507	7,711	29,036
Toyota Jidosha KK	Japan	7,040	7,962	6,317	5,525	26,844
Samsung Electronics Co. Ltd	Republic of Korea	5,873	5,865	6,666	8,243	26,647
Toshiba KK	Japan	6,087	6,055	6,030	5,422	23,594
Mitsubishi Electric Corporation	Japan	5,389	5,415	5,893	5,435	22,132
Honghai Precision Industry Co. Ltd	Taiwan, Province of China	6,783	4,842	4,254	4,539	20,418
International Business Machines Corporation	USA	4,463	4,419	5,108	5,298	19,288
Ocean’s King Lighting Science & Technology Co. Ltd	China	1,755	2,310	5,028	9,914	19,007
Sharp Corporation	Japan	4,756	5,013	5,929	3,082	18,780
Seiko Epson Corporation	Japan	5,531	5,374	3,833	3,715	18,453
Ricoh Co. Ltd	Japan	4,402	4,397	4,155	4,781	17,735
Robert Bosch GmbH	Germany	3,674	3,814	4,339	4,339	16,166
ZTE Corporation	China	5,065	4,521	3,577	2,219	15,382
Huawei Technologies Co. Ltd	China	2,124	3,240	4,644	5,117	15,125
Fujitsu Ltd	Japan	3,488	3,768	3,663	3,562	14,481
Denso Corporation	Japan	3,337	3,435	3,460	3,694	13,926
State Grid Corporation of China	China	361	1,039	3,327	8,005	12,732
China Petroleum & Chemical Corporation	China	2,436	3,092	3,394	3,802	12,724
Honda Motor Co. Ltd	Japan	3,533	3,156	3,019	2,992	12,700
Kvasenkov Oleg Ivanovich	Russian Federation	4,344	2,288	2,648	3,407	12,687
LG Electronics Inc.	Republic of Korea	3,558	2,882	2,594	2,813	11,847
Sony Corporation	Japan	3,635	3,325	2,569	2,234	11,763
Siemens AG	Germany	2,524	3,083	2,979	2,769	11,355
Hitachi Ltd	Japan	2,917	2,839	2,938	2,602	11,296
Fujifilm Corporation	Japan	3,646	3,047	2,291	1,989	10,973
NEC Corporation	Japan	3,149	2,434	2,404	2,455	10,442
Hyundai Motor Co. Ltd	Republic of Korea	2,149	2,604	2,569	2,706	10,028
Hongfujin Precision Industry (Shenzhen) Co. Ltd	China	2,799	2,840	2,475	1,754	9,868
Zhejiang University	China	2,111	2,217	2,380	2,780	9,488
General Electric	USA	2,235	2,609	2,436	1,995	9,275
Korea Electronics Telecomm	Republic of Korea	1,752	1,996	2,694	2,558	9,000
Dainippon Printing Co. Ltd	Japan	1,908	2,105	2,366	2,175	8,554
Nippon Telegraph & Telephone	Japan	2,009	2,099	2,067	2,262	8,437
Daimler AG	Germany	1,986	2,131	2,147	2,034	8,298
Sumitomo Electric Industries	Japan	1,895	2,031	1,959	1,820	7,705
Tsinghua University	China	1,643	1,779	2,125	2,060	7,607
LG Display Co. Ltd	Republic of Korea	1,963	1,867	1,754	1,918	7,502
Brother Ind Ltd	Japan	1,951	2,000	1,766	1,719	7,436
Mitsubishi Heavy Ind Ltd	Japan	1,755	1,846	2,059	1,642	7,302
Samsung Electro Mech	Republic of Korea	1,659	1,868	1,926	1,702	7,155
Kyocera Corporation	Japan	1,923	1,956	1,798	1,461	7,138
LG Innotek Co. Ltd	Republic of Korea	2,103	2,547	1,480	934	7,064
Microsoft Corporation	USA	2,291	1,978	1,357	1,409	7,035
Posco	Republic of Korea	1,314	1,723	1,973	1,798	6,808
Fuji Xerox Co. Ltd	Japan	1,744	1,435	1,708	1,507	6,394
GM Global Tech Operations Inc.	USA	1,597	1,742	1,546	1,236	6,121
Schaeffler Technologies GmbH & Co. Kg	Germany	1,193	1,538	1,556	1,743	6,030
Nippon Kogaku KK	Japan	1,474	1,562	1,645	1,276	5,957
Harbin Institute of Technology	China	1,168	1,146	1,574	2,065	5,953
Shanghai Jiao Tong University	China	1,135	1,338	1,573	1,763	5,809
Nissan Motor	Japan	963	1,238	1,673	1,825	5,699
Southeast University	China	961	1,304	1,433	1,939	5,637
Hyundai Heavy Ind Co. Ltd	Republic of Korea	747	1,393	1,946	1,437	5,523
Samsung Display Co. Ltd	Republic of Korea	7	983	1,671	2,791	5,452
Sanyo Electric Co.	Japan	2,033	1,887	931	510	5,361
Konica Corporation	Japan	646	327	2,211	2,147	5,331
Sumitomo Chemical Co.	Japan	1,596	1,708	1,304	662	5,270
Toppan Printing Co Ltd	Japan	1,384	1,299	1,312	1,268	5,263
Hewlett Packard Development Co.	USA	1,107	1,147	1,288	1,566	5,108
Tencent Technology (Shenzhen) Co. Ltd	China	453	829	1,889	1,905	5,076
LG Chemical Ltd	Republic of Korea	643	903	1,345	2,178	5,069
JFE Steel KK	Japan	1,137	1,494	1,260	1,010	4,901
Sankyo Co.	Japan	686	767	1,548	1,872	4,873
Google Inc.	USA	435	1,189	1,828	1,421	4,873
Renesas Electronics Corporation	Japan	1,567	1,446	1,150	612	4,775
Sumitomo Wiring Systems	Japan	1,008	1,128	1,199	1,358	4,693
Tianjin University	China	749	1,015	1,294	1,572	4,630
Bridgestone Corporation	Japan	1,471	1,386	908	848	4,613
Peugeot Citroen Automobiles SA	France	1,209	1,213	1,149	970	4,541
Samsung Heavy Ind	Republic of Korea	1,039	1,050	1,314	1,131	4,534
Beihang University	China	1,007	1,112	1,128	1,262	4,509
Lenovo (Beijing) Co. Ltd	China	260	608	1,854	1,786	4,508
South China University of Technology	China	773	955	1,231	1,450	4,409
Yazaki Corporation	Japan	1,074	1,093	1,021	1,116	4,304
Peking University	China	904	993	979	1,316	4,192
Olympus Corporation	Japan	1,197	1,188	911	884	4,180
Intel Corporation	USA	544	1,443	1,170	1,013	4,170
Jiangnan University	China	678	992	1,281	1,219	4,170
Casio Computer Co. Ltd	Japan	1,226	929	1,008	998	4,161
Murata Manufacturing Co.	Japan	940	1,026	1,009	1,157	4,132
Kyocera Document Solutions Inc.	Japan	148	1,100	1,235	1,603	4,086
Telefonaktiebolaget LM Ericsson (Publ)	Sweden	831	1,009	1,121	1,058	4,019
Korea Advanced Inst Sci & Tech	Republic of Korea	1,015	1,006	1,101	856	3,978
Kao Corporation	Japan	1,025	972	1,016	906	3,919
Daikin Ind Ltd	Japan	838	1,008	1,140	856	3,842
Kyoraku Sangyo KK	Japan	1,157	865	741	1,076	3,839
Hyundai Mobis Co. Ltd	Republic of Korea	859	847	1,228	880	3,814
Ford Global Tech LLC	USA	683	660	874	1,579	3,796
Taiwan Semiconductor MFG	Taiwan, Province of China	567	787	1,054	1,358	3,766
SK Hynix Inc	Republic of Korea	661	1,083	1,199	776	3,719
BOE Technology Group Co. Ltd	China	139	474	1,233	1,863	3,709
JTEKT Corporation	Japan	731	942	1,004	973	3,650
Hyundai Steel Co.	Republic of Korea	1,044	986	1,014	601	3,645
Toray Industries	Japan	810	898	959	970	3,637
Konica Minolta Business Tech	Japan	1,856	1,713	32	2	3,603
Inventec Corporation	Taiwan, Province of China	1,262	900	671	713	3,546
Nitto Denko Corporation	Japan	793	887	921	888	3,489
Jiangsu University	China	462	523	961	1,509	3,455
Toyota Ind Corporation	Japan	464	730	1,236	1,022	3,452

Sources: WIPO Statistics Database and EPO PATSTAT database, October 2016

In 2010–13, the top three universities in patent families worldwide occupied positions thirty, thirty-seven, and fifty-one among the top applicants. These universities are Zhejiang University (9,488 patent families), Tsinghua University (7,607) and Shanghai Jiao Tong University (5,809). Two public research institutes took positions thirty-two and fifty: the Korea Electronics Telecomm (9,000) and the Harbin Institute of Technology (5,953).

Table 3.6 shows the top five university and public research institute applicants in patent families for selected origins in 2010–13. The top five university and public research institute applicants in China each created between 5,000 and 10,000 patent families during this four-year period. As shown in Table 3.5, all top five university and public research institute applicants from China are among the top 100 applicants in patent families worldwide.

Table 3.6 Top five university and public research institute patent applicants worldwide by selected origins, 2010–13

Applicant	Origin	2010	2011	2012	2013	Total number of patent families, 2010-13
Zhejiang University	China	2,111	2,217	2,380	2,780	9,488
Tsinghua University	China	1,643	1,779	2,125	2,060	7,607
Harbin Institute of Technology	China	1,168	1,146	1,574	2,065	5,953
Shanghai Jiao Tong University	China	1,135	1,338	1,573	1,763	5,809
Southeast University	China	961	1,304	1,433	1,939	5,637
CEA	France	585	634	665	731	2,615
CNRS	France	484	485	516	532	2,017
INSERM	France	58	129	119	172	478
Univ Claude Bernard Lyon	France	39	31	52	49	171
Centre Nat Etudes Spatiales	France	34	41	45	38	158
Fraunhofer Ges Forschung	Germany	434	441	491	523	1,889
Deutsches Zentrum fur Luft und Raumfahrt	Germany	232	205	222	238	897
Univ Dresden Tech	Germany	75	78	78	26	257
Max Planck Gesellschaft	Germany	82	60	60	53	255
Karlsruhe Inst Technologie	Germany	58	59	51	16	184
Nat Inst of Adv Ind & Tech	Japan	801	664	677	628	2,770
Tokyo University	Japan	379	364	327	408	1,478
Tohcko University	Japan	365	337	324	300	1,326
Osaka University	Japan	243	226	272	256	997
Kyoto University	Japan	212	210	224	235	881
Korea Electronics Telecomm	Republic of Korea	1,752	1,996	2,694	2,558	9,000
Korea Advanced Inst Sci & Tech	Republic of Korea	1,015	1,006	1,101	856	3,978
SNU R&DB Foundation	Republic of Korea	621	550	609	599	2,379
Yonsei University	Republic of Korea	535	552	577	611	2,275
Univ Korea Res & Bus Found	Republic of Korea	494	518	509	473	1,994
US Navy	USA	231	204	92	65	592
Northwestern University	USA	73	103	91	167	434
US Army		165	126	61	64	416
Massachusetts Institute of Technology	USA	88	76	56	33	253
Wisconsin Alumni Res Found	USA	40	52	54	98	244

Sources: WIPO Statistics Database and EPO PATSTAT database, October 2016

Note: A patent family is defined as patent applications interlinked by one or more of: priority claim, PCT national phase entry, continuation, continuation-in-part, internal priority, and addition or division. Patent families include only those associated with patent applications for inventions and exclude patent families associated with utility model applications.

Each of the top five university and public research institute applicants in the Republic of Korea had between about 2,000 and 9,000 patent families in 2010–13. Three university and public research institute applicants in Japan created more than a thousand patent families during 2010–13, while two public research institutes in France and one in Germany were also above the 1,000 mark.

The number of patent families created worldwide in 2013 was higher than that in 2010 for nineteen of the thirty university and public research institute applicants listed in Table 3.6, including all the top five for China and France. Compared to 2010, the number of patent families created in 2013 more than doubled for Southeast University of China, INSERM of France, and Northwestern University of the U.S.

3.4.4 By IP Office

In terms of the absolute number of nonresident university and public research institute patent applications, the top destinations over the past ten years have been the State Intellectual Property Office of China (SIPO), the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), the Japan Patent Office (JPO), the Canada Intellectual Property Office (CIPO), and the Korea Intellectual Property Office (KIPO) (Figure 3.18).Footnote ¹⁴ Interestingly, the nonresident share of total university and public research institute patent applications is much higher at the JPO (58.6 percent), US PTO (54.0 percent), and EPO (49.5 percent) than at SIPO (13.1 percent) and KIPO (7.0 percent) (see Figure 3.19).

Figure 3.18 Nonresident university and public research institute patent applications for selected patent offices, 2006–15

Source: WIPO Statistics Database and EPO PATSTAT, January 2017

Figure 3.19 Share of nonresident university and public research institute patent applications for selected offices, 2006–15

Source: WIPO Statistics Database and EPO PATSTAT, January 2017

In the period 2006–15, the main sources of patent applications going outside a country were the U.S., France, Germany, the Republic of Korea, Japan, and China (Figure 3.20). However, the share of patent applications filed abroad by university and public research institute applicants was highest for the following countries of origin (Figure 3.21): Israel (90.9 percent), France (69.8 percent), the United Kingdom (66.1 percent), the U.S. (62.9 percent), Germany (61.7 percent), Canada (59.6 percent), Italy (57.5 percent), South Africa (56.3 percent), and India (46.1 percent).

Figure 3.20 Patent applications filed abroad by universities and public research institutes for selected origins, 2006–15

Source: WIPO Statistics Database and EPO PATSTAT, January 2017

Figure 3.21 Share of patent applications filed abroad by type of applicant, selected origins, 2006–15

Source: WIPO Statistics Database and EPO PATSTAT, January 2017

3.5 Conclusion

In this chapter, we proposed a methodology for measuring academic patents. Using WIPO’s PCT and the EPO’s PATSTAT data, we provided a relatively comprehensive picture of global academic patenting data.

We showed that global patenting by public research institutes and universities has increased in the last thirty-five years and the map of the main actors has changed significantly. The main findings can be summarized as follows.

The main actors in global patenting are still private sector businesses, but university and public research institute applications are surging as important innovation drivers.

The biggest trend over the last thirty-five years has been a shift in university and public research institute patenting dominance from Europe and the U.S. to Asia.

Applications by universities recorded in the PATSTAT and PCT databases were concentrated in science-based technology fields, especially pharmaceuticals and the biological sciences.

In the middle-income group of economies, universities hold more patents than public research institutes, while in the high-income group public research institutes tend to patent more than universities.

However, it is important to remember that there are numerous factors that can contribute to a university or public research institute’s proclivity to patent. A strong focus on science, technology, engineering, and mathematics, public policies that govern IP ownership between university and industry, as well as other policies that enhance the use of patents are all likely to influence the patenting activities of universities and public research institutes across countries (Reference Perkmann, Tartari and McKelveyPerkmann et al. 2013).

Nevertheless, while there are many limitations in using patent data and the extent to which it measures innovativeness, we contend that these data are useful in helping to identify potential weaknesses and highlight the strengths of universities and public research institutes.