Part I - Setting the Context

1 The Evolving Role of Public R&D and Public Research Organizations in Innovation

Suma Athreye and Sacha Wunsch-Vincent ^*

1.1 The Growth in Policies to Leverage Public R&D

1.1.1 Why Invest in Public R&D?

Science has consistently been shown to be a fundamental driver of technological progress and economic growth and a source of innovation to the business sector (Jaffe 1989; Adams 1990; Cohen et al. 2002). Its importance for economic progress has grown due to an increase in the role of knowledge as a driver of competitiveness in global markets and from emerging technologies that have opened up new opportunities for development. The increasingly science-based nature of modern technological advances has made interaction with science central to innovation.¹ Universities and public research institutes are crucial to both the discovery of new technology and the training of students in new techniques and technological developments, with the attendant economic advantages.

Firms and other innovators depend on the contributions of public research and of future scientists to produce innovations of commercial significance (see Nelson 2004). Basic research in science also serves as a roadmap for firms, facilitating the identification of promising avenues for innovation and avoiding the duplication of effort by companies. Close interaction with public research enables firms to monitor scientific advances that could transform their technologies and markets. It also facilitates joint problem solving.

In light of the value of research to many firms rather than to one particular firm or entity, economists have traditionally seen knowledge produced by universities as a public good. Indeed, university knowledge has all the hallmarks of a public good – first, the economic value attached to certain kinds of basic and other research cannot be fully appropriated by the actor undertaking the research, not least because some of it may take several years to emerge. Second, the economic value of such knowledge is often difficult or impossible to judge ex ante. As a result, without subsidy, firms would tend to underinvest in the funding of research, in particular in fields that show little prospect of near-term profitability. To avoid this underinvestment in science and research, governments have funded universities to conduct teaching and basic research (the two traditional missions of the university). Scientists are thus able to pursue blue-sky research without the pressure of immediate business considerations. The reward system is based on the scientist’s publication and dissemination record, and not on considerations of any kind of private profitability or income.

In many countries, intermediate institutions, in the form of public sector institutions and laboratories, were also set up and funded by government, in order to conduct translational research that could directly benefit industry. Such public research institutes have been important in the history of many high-income countries (the United States of America (U.S.), the United Kingdom) and continue to be important in others (Germany and the Republic of Korea). Scholars such as Nelson, Freeman, and Lundvall see universities and public research institutes as playing a key role in shaping national innovation systems and in the growth and training of scientists more broadly. This is because the magnitude and direction of public research and development (R&D) influences the broader innovation system through three mechanisms: providing human capital and training, advancing knowledge through public science, and through activities to transfer knowledge to economic actors. Recent experience with the software industry has shown that many middle-income countries whose universities only performed the teaching mission managed to accumulate human capital in excess of their developmental needs. These countries were also the most able to benefit from the sudden opening of global demand for software programmers (Arora and Gambardella 2005).

Economic studies have examined the impact of public R&D on business innovation. While imperfect, aggregate studies have found that academic research, and basic research in particular, has a positive effect on industrial innovation and industry productivity. Importantly, although public R&D does not directly contribute to economic growth, it has an indirect effect via the stimulation of increased private R&D. In other words, “crowding in” of private R&D takes place as public R&D raises the returns on private R&D.

Studies examining social rates of return to public R&D are more recent. Social returns to public R&D are often studied as the effect of public R&D on private sector productivity, and are estimated to have a (median) rate of 20 percent, which is smaller than the impact of private R&D on private sector productivity (estimated to be between 30 and 45 percent). Econometric studies at the firm and country level provide less conclusive results as to the positive impact of public R&D on private productivity than estimates at the industry level. A more intriguing result in the UK context found that the rate of return of public council funding (i.e., grants to industry often in collaboration with university, distributed through research councils) had higher social rates of return than direct public sector R&D, often two to three times the rate of return suggested by private R&D.

To some extent, the public good argument for public sector R&D does suggest that we will find such results. To recall, in the case of most public goods, private rates of return are expected to diverge from social rates of return. Public sector investments in R&D are in basic R&D that takes more than seven years to translate into commercial products and needs more private investment in R&D to be fully absorbed in industry. In contrast, private R&D has a gestation lag of about three years, is in applied areas that are less technologically risky, and is oriented toward readily available (or creatable) markets.

Several empirical issues also contribute to the observed result that public R&D does not show a strong direct impact on business innovation and economic growth. Given the many channels of knowledge transfer from public science, estimating all of the economic effects of public R&D is challenging. Transactions rarely leave a visible trace that can be readily identified and measured. Second, the contribution of public R&D can also take a long time to materialize and this time lag can differ by sectors of activity. Finally, the noneconomic impact of public research in areas such as health, and others, is even harder to identify.

1.1.2 The New Rationale for Public Support of “Third Mission” Policies at Universities

Public R&D suffers from a key limitation when compared to private R&D. When firms undertake R&D they usually have an idea of the type of knowledge they need to produce and a commercialization strategy that is directly attached to their R&D expenditure plans. This rarely happens with public sector R&D, with the people undertaking R&D working in a separate organization from the potential users of the knowledge. Consequently, there is always a scope for discoveries, even those with commercial potential, to fail to be commercialized.² In other words, public research may produce a lot of inventions, but no significant innovations. It has also led to accusations that academic research lives in an ivory tower, divorced and disengaged from the real world and its problems.

Since the late 1970s, many countries have changed their legislation and created support mechanisms to encourage interactions between universities and firms, including through knowledge transfer (see Van Looy et al. 2011). Placing the output of publicly funded research in the public domain is no longer seen as sufficient to generate the full benefits of the research for innovation (see OECD 2003; Wright et al. 2007). In high-income countries, policy approaches promoting increased commercialization of the results of public research have included reforming higher education systems to include third mission activities creating clusters, incubators, and science parks; promoting university–industry collaboration; instituting specific laws and institutions to regulate knowledge transfer; and encouraging public research organizations to file for and commercialize their IP. The transformation of research organizations into more entrepreneurial organizations is also taking place by increasing the quality of public research, creating new incentives and performance-linked criteria for researchers, enhancing collaboration of universities and public research institutes with firms, and setting up mechanisms for formal knowledge transfer (see Zuñiga 2011).

Contrary to popular perception, it was not the U.S. but Israel that was the first country to implement IP policies for several of its universities in the 1960s. However, in 1980 the US Bayh-Dole Act was the first dedicated legal framework to institutionalize the transfer of exclusive control over federal government-funded inventions developed by universities and businesses. The shift and clarification of ownership over these inventions lowered transaction costs as permission was no longer needed from federal funding agencies, and because this gave greater clarity to ownership rights and therefore greater security to downstream – sometimes exclusive – licensees. For instance, the Act also contains rules for invention disclosure and requires institutions to provide incentives for researchers. It also contains march-in provisions reserving the right of government to intervene under some circumstances.

Several European, Asian, and other high-income countries have adopted similar legislation, in particular from the latter half of the 1990s onwards (see Montobbio 2009; Geuna and Rossi 2011). In Europe, in many cases, the challenge was to address the established situation according to which IP ownership was assigned to the faculty inventor – the professor’s privilege – or to firms that funded the research (see Cervantes 2009; Foray and Lissoni 2010). Since the end of the 1990s, most European countries have been moving away from inventor ownership of patent rights toward university or public research institute ownership.³ European policy efforts have sought to increase both IP awareness within the public research system and the rate of commercialization of academic inventions. In Asia, Japan was the first to implement similar legislation in 1998 and, in 1999, shifted patent rights to public research organizations. The Republic of Korea implemented similar policies in 2000.

Policymakers keen to bolster the effectiveness with which publicly funded research can foster commercial innovation today have a rich menu of options thanks to the experimentation with such policies in many countries (see Just and Huffman 2009; Foray and Lissoni 2010). A number of middle- and low-income countries have also moved in this direction (for more details, see Zuñiga 2011). In spite of the lack of an explicit policy framework, many of these countries have put in place general legislation regulating or facilitating IP ownership and commercialization by research organizations.⁴ There are four distinct sets of approaches used by countries. In the first set, there is no explicit regulation but rather general rules defined in the law – mostly in patent acts – or legislation regulating research organizations or government funding. A second model consists of laws in the form of national innovation laws. A third, adopted in Brazil, China, and more recently in economies such as Malaysia, Mexico, the Philippines, and South Africa, builds on the model of high-income countries that confers IP ownership to universities and public research institutes, spurring them to commercialize. Fourth, some countries, for example Nigeria and Ghana, have no national framework but rely on guidelines for IP-based knowledge transfer.

Large middle-income economies, such as Brazil, China, India, the Russian Federation, and South Africa, have already implemented specific legislation or are currently debating its introduction. China was among the first to adopt a policy framework in 2002.⁵ In addition, a significant number of countries in Asia – in particular Bangladesh, Indonesia, Malaysia, Pakistan, the Philippines, and Thailand – and in Latin America and the Caribbean – Mexico in particular and, more recently, Colombia, Costa Rica, and Peru – have been considering such legislation.⁶ However, only Brazil and Mexico have enacted explicit regulations regarding IP ownership and university knowledge transfer so far. In India, institutional policies have recently been developed at key national academic and research institutes, complementing legislative efforts that aim to implement university IP-based knowledge transfer rules (see Basant and Chandra 2007).

In Africa, most countries other than South Africa have neither a specific law on IP ownership by research organizations nor any knowledge transfer laws. However, several countries have started to implement policy guidelines and to support knowledge transfer infrastructure. Nigeria and Ghana, for instance, do not have specific legislation but are both in the process of establishing knowledge transfer offices (KTOs) in all institutions of higher education.⁷ Algeria, Egypt, Morocco, and Tunisia have been working on drafts for similar legislation. In 2010, South Africa implemented the Intellectual Property Rights from Publicly Financed R&D Act, which defines a number of obligations ranging from disclosure, IP management, and inventor incentives, to the creation of KTOs and policies regarding entrepreneurship.

Studies conducted on the group of high-income countries reveal a few important lessons.⁸ First, despite the general trend toward institutional ownership and commercialization of university and public research institute inventions, a diversity of legal and policy approaches persists, in terms of both how such legislation is anchored in broader innovation policy and the specific rules on the scope of university patenting, invention disclosure, incentives for researchers (such as royalty sharing), and whether certain safeguards are instituted to counteract the potentially negative effects of patenting.⁹ Second, the means to implement such legislation, as well as the available complementary policies to enhance the impact of public R&D and to promote academic entrepreneurship, vary widely. Finally, legal changes alone have not started or contributed to sustained patenting by public research organizations. In the U.S., university patenting is also driven by growing technological opportunities in the biomedical and other high-tech fields, as well as a culture change favoring increased university–industry linkages (see Mowery et al. 2001).

1.1.3 Conflicts and Tradeoffs between the Old and New Rationales for Public R&D

Although, in theory, this rich menu of “third mission” policies was intended to amplify the impact of public R&D, in practice, many countries adopting these policies were also looking to cut back on public spending and intended that budget cuts to universities should be compensated by proactive approaches to revenue generation (Vincent-Lancrin 2006). There is increasing evidence that countries seek to recover the full economic cost of research activity in order to allow research organizations to amortize the assets and overhead, and to invest in infrastructure at a rate adequate to maintain future capability. Paradoxically, support for the third mission may have come at the expense of cutbacks in funding for public R&D itself. Thus, in practice, the policies of increasing commercialization of university research and industry funding of public research were often adopted in the context of a tightening of public investments in R&D. Thus, far from amplifying the economic effect of public investments in R&D, commercialization of university research very quickly became a substitute for public funding of research and so its net effect on the economy-wide diffusion of technology may be difficult to gauge.

Second, universities have always regarded themselves primarily as centers of learning, where new knowledge is created and curated through research, and ultimately disseminated via teaching. They see themselves as upholding the four Mertonian norms of communism (common ownership of scientific outputs without resort to secrecy), universalism (universal scientific validity irrespective of who the source of scientific output is), disinterestedness (acting in common scientific interest rather than for personal gain), and organized scrutiny (critical scrutiny of scientific output before acceptance). Academic researchers are a self-selected group who are largely driven by the same set of norms in the pursuit of their individual research careers.

Commercialization activities contradict at least two of the four Mertonian norms, given that they are motivated by private ownership of intellectual property and private gain. This leads to a fundamental tradeoff between the ideal of pure scientific exploration versus profit-driven commercial exploitation. Furthermore, pure scientific exploration is essential to the first mission of the university, the provision of education. Universities caught between scientific exploration and exploitation will struggle to simultaneously reconcile both these aims. Indeed, management science teaches that most organizations struggle both to explore new knowledge and to exploit existing knowledge at the same time (organizational ambidexterity), as the two sorts of activity require a different type of management and entail different risks.

Public research institutes were set up as specialized intermediaries to fulfill the commercialization function: to take up frontier science from universities and adapt them to the needs of local communities and industry. More recently, they have been in (possibly) terminal decline, even in countries where they have been quite successful. The reasons for this decline are not clear and probably deserve a book of their own to explore more rigorously, but it is likely that shifting the locus of commercialization from these specialized intermediaries to universities driven by Mertonian norms may have been inefficient in countries where the institutional frameworks to transfer knowledge directly from universities were still immature and poorly developed. Third mission activities in many countries, however, came at the expense of public research institutes. Whether universities’ third mission activities can and should replace public research institutes remains an underexplored question.

Lastly, while nobody denies that the payoffs of academic research are maximized when the private sector uses and builds on research carried out in the public sector, these are not one-way exchanges from universities to firms. Industrial research complements and also guides more basic research. It is also a means of “equipping” university scientists with new and powerful instruments. For such knowledge transfer to work, firms need to be able to assimilate and exploit public research. This capability often requires firms to actively engage in upstream research and actively participate in science (see Cohen and Levinthal 1989). In middle- and low-income countries, even large firms may lack this capability, while in high-income economies small firms may behave in this way. Policies to promote outward knowledge transfer from universities and public research institutes are likely to fail if local firms lack sufficient absorptive capacity.

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.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 Arora et al. 2015). On average, in 2009, the public sector performed more than three-quarters of all basic research in high-income economies.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:

• a lower level of S&T;

• low research and innovation capabilities of domestic firms, with the result that government and international donors are often the main funders of S&T and national public research institutes are the main R&D performers;

• less developed human capital for S&T activity, particularly a low number of scientists in firms and the best domestic scientists moving abroad (“brain drain” effect);

• lower quality research and relevance of public research to the business sector;

• limited science–industry linkages, explained by a low absorptive capacity of firms combined with an ensuing lack of “business” demand for S&T;

• a lack of policies and structures to facilitate academic and other startups; and

• constrained access to financing as a barrier to the development of radical or early-stage innovations.

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; Zuñiga 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.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 Navarro 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.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.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:

• lack of clear knowledge transfer policies for universities and public research institutes;

• weak operative guidelines on patenting, for example on disclosure and commercialization of IP at the institutional level;

• little awareness about and few incentives for researchers to participate in IP-based knowledge transfer; and

• absence of or inadequate resources for KTOs, with staff lacking the necessary skills and experience related to IP and commercialization.

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; Zuñiga 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 Guellec 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?

Comment 1.1

Fabio Montobbio

This project provides an extremely interesting comparison of research and technological transfer activities across different countries, and, in parallel, promotes the use of a set of metrics. The approach takes its departure from the analysis of the systems of innovation that encompasses the main actors and institutions involved in the process of knowledge transfer. It allows a fine-grained analysis of the different details of the context in which knowledge transfer takes place, exploiting a mixture of quantitative and qualitative analysis. In so doing, it provides a very valuable tool to help policymakers to measure the research, transfer, and commercialization activities in order to design new innovation policy approaches and sustain successful practices. On the one hand, it is important to learn about successful examples and best practices, and, on the other, efforts at emulation could have modest success if not coupled with deep attention to the underlying structural differences among the innovation systems of the different countries. Taking on board the systemic approach, I would like first to discuss my view on possible ways to disentangle the complexity of the different environments in which knowledge transfer takes place and, second, to discuss how normative statements can arise from this perspective. In particular, I would like to underline first how the different systems of innovation depend on a set of structural characteristics, namely: the intensity of the research effort, the technological specialization, and the industrial structures. Second, I would like to underline how systemic failures may occur at different levels, and fixing those failures naturally includes a quite heterogeneous set of policy interventions.

The first comment is that knowledge transfer practices are affected by a set of structural characteristics of the countries. So when addressing a comparison of knowledge transfer practices across countries, it is important to take into account the relative strengths and weaknesses of the public and private systems of R&D. For example, it emerges that the six countries considered in this project have very different R&D/GDP expenditures (OECD 2017a). In 2015 in China, the R&D/GDP ratio was 2.1 percent, in Germany, 2.9 percent, in the Republic of Korea, 4.2 percent, in the United Kingdom, 1.7 percent, in Brazil, 1.2 percent, and, finally, in South Africa 0.7 percent. The growth of R&D intensity has been impressive for China and Republic of Korea. For these countries the figures were 0.5 percent and 2.2 percent in 1995, respectively. It is also worth noting that the share of gross domestic expenditure on R&D funded by the government is smaller in the Republic of Korea and China (21 percent and 23 percent, respectively, in 2015), denoting a relevant and increasing role of the R&D funded by the private sector. In parallel, in Germany and the United Kingdom, the share of R&D funded by the government is about 28 percent. This share for South Africa is 42 percent (OECD 2017b). For Brazil, UNESCO data show that in 2014 the investment in R&D was BRL 65 billion, and almost two-thirds was funded by the government.

The figures given here show that countries like Brazil and South Africa that have a lower R&D/GDP ratio are also the ones that display a weaker role for private sector R&D. The relative role of the private sector, in turn, is associated with the profile of the country in terms of technological specialization and with processes of structural change. Technological capabilities tend to be associated to the technological specialization of countries. For example, Brazil and South Africa did not undertake a major process of structural change as China did. In China, a high growth in technological capabilities is associated with a substantial shift toward the electronic and telecommunications equipment industry and computers. These industries are a major driver of the aggregate growth of national and international patenting of the country (e.g., Malerba et al. 2011; Hu et al. 2017).

The distribution of technological capabilities in a country innovation system depends on the presence of different types of organization. In particular, it is important to have a balanced evolution of the different actors, with a growth of the presence of both multinational corporations and domestic innovators. China, for example, has a growing presence of both domestic and foreign companies in electronics. This confirms the major role played by ICT in the growth of the Chinese economy, as well as the role played by foreign companies in China. In parallel, the impressive growth of patenting activity at the Chinese Patent Office is mainly driven by new entrants: firms that were not systematically applying for patents in the past (Hu et al. 2017). The dynamism of the domestic private sector witnesses the ability to absorb foreign knowledge and to benefit from the big investments in public institutions and infrastructures documented by Chapter 8 in this book.

An additional important aspect is the coherence between the technological activities of the different actors within the country innovation system. In Brazil and South Africa, universities and public research institutes tend to innovate and patent relatively more in chemicals, pharmaceuticals, and biotechnology. This presence has been growing over time and the Brazilian government has supported research in pharmaceuticals and biotechnology in both universities and public research institutes (see Chapter 7 of this volume and Ferrer et al. 2004). In parallel, the specialization profile of domestic multinational companies tends to remain stable in more traditional sectors such as consumer goods, engineering, and transport. Similar considerations could be put forward for South Africa. This potential mismatch between the activities of universities and public research institutes and the technological profile of the companies suggests that well-tuned knowledge transfer policies and practices are key for a balanced and sustainable path of growth (see Chapter 9).

Other structural aspects that might affect the way knowledge transfer takes place are the quality of the research system and the quality of the institutional framework. This book provides an excellent guide to assessing and comparing the complexities of the different countries. In particular it is worth noting that, in general, it is quite difficult to understand all the sources of public research funding in a country. Public funding passes through different levels of governance (e.g., state, regions, municipalities) and different types of organization (e.g., public/private/nonprofit/ foundations). There may be public research institutes that depend entirely on regional administrations or are owned by other public non-research entities. A quite heterogeneous set of government acts channels public money into knowledge transfer activities. All these country-specific features affect the level of knowledge transfer activity but also the channels used and the quality of that transfer. For example, different public research intitutes may have different types of constraint in terms of patenting activity, and they may exploit the formal and informal channels of knowledge transfer to different degrees (see, for example, the discussion in the chapters on Germany and the UK in Chapters 5 and 4, respectively).

Finally, geographical aspects play an important role. The presence of innovative geographically concentrated clusters could provide, on the one hand, specific agglomeration effects and, on the other, regional imbalances. Knowledge transfer practices may vary dramatically according to whether companies and universities and public research institutes are colocated in a technological cluster or in a science park or whether the region is characterized by the prevalence of rural areas.

My second comment discusses how normative statements can arise from this perspective, and I would like to underline that a precise qualitative and qualitative description of the systems of innovation shows where the successes and the potential failures of the processes and practices of knowledge transfer actually are. This book addresses the issue of knowledge transfer with a broad view that institutions and regulations are constitutive elements of the innovation system. In particular, a substantial effort of the different studies is dedicated toward understanding the different specific regulations that different countries adopted. These regulations generate the specific conditions under which firms, individuals, universities, and public research institutes own immaterial assets and the new knowledge they produce. These regulations should create incentives to invest in new knowledge and in parallel facilitate diffusion and commercialization. It is emphasized that the process of knowledge transfer is characterized by a set of formal and informal channels, and these channels depend on different types of regulation: hard regulations and soft regulations (Borrás and Edquist 2014). Soft regulations are not legally binding and hard regulations are a set of rules with some mechanism for monitoring and promoting compliance with the rules.

So systemic failures may take place at different levels, and fixing those failures naturally includes a quite heterogeneous set of policy interventions. The interesting aspect is how to frame precisely normative issues with this approach. Actually, the approach is very flexible and allows, through detailed case studies, the identification of specific problems in the innovation system that could unfold at different levels. For example, at the policy level it is difficult in many cases to be able to clearly argue whether there is the need for more R&D or more knowledge transfer (or more knowledge transfer offices) or more university–industry cooperation. In many cases, knowledge transfer policies are simply imported from other countries without a precise understanding of the bottlenecks in the system (Ejermo and Toivanen 2018). However this approach – beyond simple policy prescriptions on market failures – allows us to identify different types of specific failure and problem in the system and allows for the identification of specific policy answers (Edquist 2011).

This book provides many different examples of potential design failures, network failures, or capability failures. Design failures occur when a regulation generates incentives that are not compatible with the policy objective; network failures happen because of a lack of linkages between actors. This creates a weak exploitation of complementarities and learning. We observe a network failure also when firms and companies in a country are tightly connected but remain locked in and miss out on new outside developments. Capability failures take place when firms, universities, public research institutes, and, in particular, KTOs lack the capabilities to learn rapidly and effectively and hence remain locked into existing practices. This conceptual framework therefore provides detailed guidance on how to evaluate existing knowledge transfer policies, practices, and outcomes, to identify in a comparative way potential failures and problems, and, finally, to design specific targeted policy intervention.

Comment 1.2

Lien Verbauwhede Koglin

Globally, there is a growing interest in the role of universities and public research institutes in the alchemy of innovation, the emphasis being on how they can make more systematic efforts to unlock the commercial value of their research. While many of these feel it is imperative that their knowledge transfer activities work to recover costs, from my experience, revenue generation, in most cases, is not and in my view should not be the primary motivation. The reasons these institutes engage in knowledge transfer is to advance education and research; and at the same time it helps to ensure that public investment in research is impactful, that it contributes to broader socioeconomic development objectives. However, the going is tough, even in high-income countries and the entrepreneurial character of these institutes remains the subject of academic scrutiny. The chapter inspires a deeper understanding of this critical area by examining the evolving role of institutes in national innovation systems. It also examines the impact of legislative and policy initiatives that promote protection of inventions through patenting and their commercialization through licensing and startup formation.

WIPO has developed several programs in an endeavor to help public research organizations set the right institutional policies in order to successfully harness public research for innovation and contribute to socioeconomic development in their regions.¹ In this context, I have witnessed two particular trends concerning universities’ engagement with IP-based commercialization, where improved understanding and additional metrics would seem to be desirable: (1) an expansion of academic incentive schemes and (2) an increased commitment to socially responsible commercialization.

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 Evaluating Knowledge Transfer Policies and Practices: Conceptual Framework and Metrics

Anthony Arundel and Sacha Wunsch-Vincent

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)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,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 (Cohen 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 (Arora et al. 2016).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 (Van Looy et al. 2011; Curi et al. 2012; Hewitt-Dundas 2012; Calderón-Martínez and García-Quevedo 2013; Okamuro and Nishimura 2013; Hussain et al. 2014; Ranga 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 (Freitas et al. 2013).

Not surprisingly, firm involvement in knowledge transfer from public research organizations increases with the firm’s R&D intensity (Freitas et al. 2013; Okamuro and Nishimura 2013; Kafouros 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 (Okamuro and Nishimura 2013).

Albuquerque 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 (Kruss et al. 2015), whereas in middle-income countries in Asia (China, Malaysia, and Thailand) the most common methods are consultancy and research contracts (Schiller 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 (Dutrénit 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 (Rothaermel 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.

2.3.1 National and Institutional Policies and Practices to Support Knowledge Transfer

The most common national policy of direct relevance to knowledge transfer concerns the ownership of IP developed in the public research sector. In some countries, such as the U.S., national laws give ownership to the institution, other countries, Sweden, for instance, assign ownership to the inventor, while yet others, such as Canada, leave the decision to the institution.

An extensive literature exists on the factors that are linked to successful knowledge transfer by KTOs, but there is only limited research on the effect of institutional practices at the level of the university or public research institute (Barjak et al. 2015; Belenzon and Schankerman 2009). Relevant practices include:

• activities to create an institutional culture that supports knowledge transfer;

• the establishment of institutional strategies for knowledge transfer and commercialization, such as rules for transparency in contract negotiations;

• incentives for staff to disclose inventions and support knowledge transfer by working with potential licensees;

• policies that encourage academic startups, such as allowing faculty to create and own a share in a startup or to take a leave of absence, the provision of finance, and supportive infrastructure such as incubators and science parks.

Overall, the evidence stresses the importance of a well-defined IP policy. Universities with internal rules supporting the participation of researchers in knowledge transfer perform better than universities without such rules (Debackere and Veugelers 2005). Further discussion of the effect of institutional policies on knowledge transfer is provided in Chapter 10.

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 (Foray and Lissoni 2010) and the benefits of other contractual or informal channels for knowledge transfer (Rosli and Rossi 2014; Veugelers 2016).⁴ Combining informal and formal channels can have a positive effect on innovation outcomes (Siegel et al. 2003; Link et al. 2007; Grimpe and Hussinger 2013). The use of both channels could be especially important to spinoffs (Hayer 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; Zuñiga 2011).

Table 2.1 Impacts of IP-based knowledge transfer policies on universities/public research institutes and firms

	Potential benefits	Potential costs (or investments)
Public researchorganizations	1) Increased IP ownership facilitates entrepreneurship and vertical specialization Reinforces other policies aimed at academic entrepreneurship (e.g., enhancing access to finance) Licensing and other revenues (e.g., consulting) can be invested in research 2) Cross-fertilization between faculty and industry Intangible benefits to university/public research institutes’ reputation and the quality of research Helps to identify research projects with a dual scientific and commercial purpose 3) Increased student intake and ability to place students in firms	1) Diversion of time away from academic research Distorts incentives for scientists, leading to changes in the type of research that is conducted Reorganizes university processes and culture with a view to commercialization 2) IP-related establishment and maintenance costs Cost of establishing and maintaining a KTO and related IP management, including investment in expertise and human resources Cost of time on IP filings and knowledge transfer (even if contracted out to a KTO) Additional financial and reputational costs associated with defense of IP rights
Firms	1) Facilitates university–business linkages Enables firms to have access to top scientists and to collaborate in developing innovations within a clear contractual setting 2) Enables the creation of a market for ideas and contracting with universities Framework diminishes transaction costs and increases legal certainty, facilitating investment by private sector Securing an exclusive license increases incentives for further investment Ability to specialize is a competitive advantage (vertical specialization) Increases transparency through published databases on licensing and management practices Improves content of patent databases 3) Commercialization of new products, generating profits and growth	1) Barriers to access to university inventions Reduced free access to university inventions and research tools, except where they result from a sponsored contract Lack of access if another firm has secured an exclusive license 2) IP-based transaction costs and tensions in industry–university relationships University scientists lack an understanding of development costs and market needs, leading to a higher probability of bargaining breakdown IP negotiations can interfere with establishment of joint R&D and university–industry relations when institutions act as revenue maximizers with a strong stance on IP

Source: Authors

Table 2.2 Socioeconomic effects of IP-based knowledge transfer policies

	Potential benefits	Potential costs
Broader impacts on science	1) Increased impact of more focused research with potential for application 2) Improved innovation system linkages Efficient division of labor in the generation and commercialization of new inventions Private sector contribution to funding basic and applied research 3) Increase in the quality of research and education	1) Reorientation of the direction of research Overemphasis on applied, short-term, more lucrative research Less diversity in scientific disciplines as focus on patentable outcomes increases Other university missions such as teaching and training are neglected 2) Negative impacts on open science Crowds out/displaces the use of other knowledge transfer channels to industry Publication delays, increased secrecy, less sharing, including the withholding of data Decrease in international scientific exchanges 3) The prospect of income for universities/public research institutes can reduce government commitment to funding
Innovation and growth	1) Commercialization of inventions with economic and social impacts Increase in consumer welfare and business productivity via access to innovative products and processes 2) (Localized) positive impacts on R&D, technology spillovers, entrepreneurship, employment and growth 3) Higher competitive position of country in global market	1) Long-run negative effect of diverting attention away from academic knowledge production 2) Long-run negative effects of IP on open science and follow-on innovation Patenting of broad upstream inventions, platform technologies and research tools increases the cost of follow-on research and innovation Reduces the diversity of research 3) Focus on IP might inhibit rather than promote the commercialization of inventions

Source: Authors

Table 2.3 Impacts on low- and middle-income countries

Potential benefits

Potential costs

1) All the benefits mentioned above (see Tables 2.1 and 2.2) This depends, however, on the capacity of businesses to absorb and further develop university inventions – either by domestic firms or by locally present multinational firms – and whether or not these inventions are relevant to the needs of low- and middle-income countries 2) Ability to contribute to local or global markets for university inventions This depends on the capacity to generate university inventions and file patents University inventions might also attract the presence of multinational companies and their associated complementary R&D Strengthened science–industry links could help reorient research toward local needs 3) Improved visibility of public research inventions Local firms may find it easier to identify relevant inventions and academics through patent searches than through searches of academic literature

1) All the above-mentioned costs (see Tables 2.1 and 2.2), some of which are amplified given the greater resource constraints of less-developed economies Reduced or no access to critical technologies owned by universities in high-income countries Overemphasis on applied, lucrative projects may lead to less useful inventions from the point of view of low- and middle-income countries The decrease in international scientific exchanges and a reduced willingness of institutions in high-income countries to collaborate as a result of more complex IP ownership issues and secrecy High cost of obtaining international patent protection for university inventions and resulting opportunity costs

Source: Authors

2.4.1 Advantages

Due to data availability (discussed in section 4), we know more about the IP licensing model in high- and middle-income countries than other forms of knowledge transfer. Patents are the classic form of IP, but IP is also used to protect plant varieties (plant breeders’ rights), biological tissue, knowhow (protected under secrecy), industrial designs, and copyright (relevant to software outside the U.S.).⁵

Studies show that the IP licensing model has supported the emergence of new industries, such as the scientific instruments industry, semiconductors, computer software, and the nano- and biotechnology industries (Rosenberg and Nelson 1994; Zucker et al. 1998; Di Gregorio and Shane 2003). Startups based on university/public research institute IP are also more likely than established firms to commercialize new technologies that are radical, early stage, or of a general-purpose nature. However, attributing these positive impacts exclusively to the IP licensing model is difficult in the absence of research on the role of other knowledge transfer channels.

The IP licensing model has secondary benefits other than its primary objective of transferring a specific set of knowledge to one or more businesses. These other benefits depend on the ability of a patent to signal the presence of expertise within a university/public research institute via the information contained in it, which can lead to mutually beneficial collaborative and contract research agreements, placements for graduate students, funded PhD scholarships and improvements in research quality. The cross-fertilization of ideas, problems, and knowledge between universities/public research institutes and firms can facilitate joint problem solving and open up new avenues for research (Owen-Smith and Powell 2003; Azoulay et al. 2009). While this has been an ongoing trend in high-income economies over the last few decades, it has enormous potential benefits for low- and middle-income economies, particularly in building up the research capabilities of universities.

Many of the secondary benefits extend beyond unidirectional knowledge exchanges between universities or public research institutes and firms. Industrial research can complement and guide the direction of basic research. Contractual arrangements with firms can provide university scientists with funds to purchase advanced equipment and instruments.

The signaling function can also be met through publication in scientific and technical journals, but the focus of patents on inventions with commercial possibilities could have an advantage over publications, where commercial ideas could be more time-consuming (and therefore costly) for businesses to identify. Furthermore, the existence of patents signals the willingness of the institution to license knowledge.

2.4.2 Disadvantages

Open science is based on the norms of rapid disclosure of research results and an environment of knowledge sharing, co-authorship and joint projects that contribute to cumulative learning. The patenting of university inventions could have negative effects on these norms by slowing the diffusion of university inventions, including research tools. This could have an unintended effect of stifling private sector innovation (Eisenberg 1989; Heller and Eisenberg 1998; Kenney and Patton 2011).⁶ In particular, the exclusive licensing of patents to single firms could limit the diffusion of knowledge generated with public funds, reducing the diversity and number of follow-on innovations.

Moreover, strong IP policies could negatively affect other knowledge transfer channels that might be equally or more effective in supporting knowledge transfer under specific conditions. These include informal knowledge exchanges between businesses and academics as well as formal R&D collaboration, which could be affected by the need for complex negotiations over IP rights.

The nonfinancial disadvantages of close university–industry linkages include a loss of academic freedom, a decline in basic research,⁷ a shift away from research of low commercial interest, and restrictions on or delays in publication due to the interest of commercial partners in secrecy (Van Looy et al. 2004; Tartari and Breschi 2012; Muscio and Pozzali 2013). Examples have been noted of companies restricting the findings of university researchers or researchers denying others access to their data (Campbell et al. 2000; Campbell et al. 2002). Despite these examples, none of the research to date has found strong negative effects that cannot be managed with good university codes of practice (Fabrizio and Di Minin 2008; Czarnitzki et al. 2009; Grimaldi et al. 2011).

The lack of strong evidence of negative impacts could be partly due to research designs that are unable to detect problems. For example, the importance of publication delays is likely to be greatest for early-career researchers such as PhD candidates and post-doctorates who need to build up a list of publications rapidly. Yet this possible effect is missed in studies that focus on heads of research groups or university departments. This could be one reason why a study of departmental heads finds that publication delays are given a low importance ranking as a barrier to collaboration with industry, whereas the choice of research ranks much higher (Muscio and Pozzali 2013).

The risk of industry exerting an undue influence on academic research is constrained by the small share of university R&D that industry funds.⁸ In the U.S., for example, industry finances about 5 to 6 percent of all basic and applied academic R&D, respectively, although its share (and likely influence) is much higher in health-related R&D.

The adoption by universities of a proactive patenting strategy can create other disadvantages. Long delays in reaching an agreement over IP terms, or university actions to maximize their potential revenue (Alexy et al. 2009; Wadhwa 2011),⁹ can discourage university–industry collaboration. Firms can also be discouraged if institutions use a “one-size-fits-all” approach to patenting research results that ignores the evidence that patents and exclusive licensing play different roles in the development of complex versus discrete technologies (So et al. 2008).

Few studies have assessed the disadvantages of institutional IP strategies. Instead, studies show that often – and despite potential friction – university IP, collaboration, and research productivity go hand in hand. Universities that collaborate more with industry also tend to have the most patents.

The IP licensing model could have negative effect on low- and medium-income countries by raising the costs for businesses to license research tools, databases, and technologies (Boettiger and Bennett 2006; Engel 2008; So et al. 2008; Montobbio 2009). In particular, by increasing prices, stricter IP practices could hinder access to technologies in agriculture, health, and essential medicines that are of critical importance to less-developed economies (Boettiger and Bennett 2006). Another concern is that opportunities for scientific networking or collaboration between scientists in high-income and less-developed countries could be negatively affected by conflicts over university patenting strategies (Clemente 2006).

2.4.3 Minimizing the Costs of IP-Mediated Knowledge Transfer

Universities/public research institutes, funding agencies, donors, and governments have two levers for preventing or limiting the potentially negative impacts of IP-based knowledge transfer. First, the patenting and licensing of specific types of invention can be restricted. For instance, guidelines can demand that patents should be sought, and exclusive licenses attributed, only where they are a necessary condition for their commercialization. University policies and government bodies can also declare certain areas off-limits to university patenting: basic research, research tools, or technologies critical to public health in low-income countries.

Second, when inventions are patented, the type of and access to downstream licenses can be influenced by legislation or institutional policies. For instance, licensees of government-funded technologies can be required to disclose follow-on investment and the steps taken to commercialize the patent. The goal is to prevent firms from using licensed patents to block follow-on inventions by other firms. Other requirements can ensure that products derived from licensed inventions are sold to consumers or poorer countries on reasonable terms (OECD 2003; So et al. 2008). Field-of-use restrictions can be implemented to ensure that the IP is made available for future research, including to other firms. Governments can also reserve the right to practice the invention or override exclusive licensing rights (“march-in rights”).

Universities and public research institutes are experimenting with a number of interesting additional approaches, such as open IP policies. These include patenting and licensing strategies (e.g., granting firms nonexclusive rather than exclusive licenses, making licenses available for free or at low cost if used for humanitarian or not-for-profit purposes or by small firms or startups in selected technologies), and providing easier access to research tools and to copyrighted works such as teaching materials, an often-neglected IP issue.

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 (Tartari and Breschi 2012; Abreu 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.

Table 2.4 Knowledge transfer channels and data sources

1	2	3
Open science (informal)	Contractual (formal)	IP mediated (formal)
Training of firm staff by institutions, placement of postgraduates in a firm for an internship	Problem solving/consultancy with academics1	Licensing of institutions’ IP (patents, copyright, industrial designs, plant breeder’s rights, knowhow, etc.)
Hiring university graduates	Research contracts (research supported by financial or in-kind contributions from government or industry)	Spinoffs/startups using the institute’s IP
Attending conferences or workshops	Collaborative R&D projects (joint funding and participation by the public organization and government or industry)	Joint ventures using the institute’s IP
Reading academic literature
Personal contacts
Access to advanced facilities or equipment
Data sources
Surveys of firms or academics	Surveys of academics, firms, or KTOs1	Surveys of KTOs, firms, or public data sources

Source: Cohen et al. (2002); Cosh et al. (2006); Ramos-Vielba and Fernández-Esquinas (2012); Tartari and Breschi (2012)

¹ KTOs may be unaware of many private consultancies between academics and firms, particularly if academics are not legally required to report private consultancies to their institution.

With a few exceptions,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 (Cohen et al. 2002; Siegel et al. 2003; Cosh et al. 2006; D’Este and Patel 2007; De Fuentes and Dutrénit 2012; Hughes and Kitson 2012; Grimpe and Hussinger 2013; Freitas et al. 2013; Okamuro and Nishimura 2013; Dutrénit and Arza 2015; Kafouros et al. 2015; Kruss et al. 2015; Schiller 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 (Cosh et al. 2006; Zhang et al. 2016).

2.5.1 Basic Metrics of Knowledge Transfer via Licensing

Metrics include statistics and indicators. Relevant statistics for knowledge transfer include count data such as number of patent applications and the total amount of license income earned. Indicators standardize a statistic, for instance by providing the number of patent applications per 1,000 research academics in the sciences or the amount of license income earned per EUR 1 million in research expenditures. Both statistics and indicators need to refer to a defined time period such as one calendar year.

Indicators are essential for benchmarking performance. Using statistics to compare the number of invention disclosures among a group of universities would be seriously misleading if the group included universities with large differences in the number of academic staff or in the types of discipline. A university that focuses on law and the humanities is likely to have far fewer opportunities for consulting contracts than one that focuses on science, technology, and medicine.

There are three main reasons for collecting knowledge transfer metrics for licensing:

1. 1. to benchmark knowledge transfer activities, for instance to permit comparisons in performance within an institution, across institutions, or over time;

2. 2. for use in analyses to identify the factors that either support or hinder knowledge transfer; and

3. 3. to inform policy, such as determining the effect of a change in policy on knowledge transfer outcomes.

These three reasons are linked because research on the factors that support knowledge transfer can use benchmarking data as an output measure, for example, in a study of the factors that increase the number of patents or the amount of license revenue. Plus, research on “what works” can be of value in developing or improving policies to support knowledge transfer.

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.

Table 2.5 Basic metrics from KTO surveys

	Statistic	Definition1
1	Number of invention disclosures	Descriptions of inventions or discoveries that are evaluated by the KTO staff or other technology experts to assess their commercial application
2	Number of patent applications	New priority patent applications. Exclude double-counting, such as a patent application for the same invention in more than one patent jurisdiction
3	Number of patents granted	Technically unique patents granted. Count a patent grant for the same invention in two or more countries as one technically unique patent. If a technically unique patent grant has been counted in a previous year, it may not be counted again
4	Number of research agreements	All contracts where a firm funds the university or public research institute to perform research on behalf of the firm, with the results usually provided to the firm. Include collaborative agreements where both partners provide funding and share the results. Exclude cases where the firm funds a research chair or other research of no expected commercial value to the firm
5	Number of licenses executed	Include all licenses, options and assignments (LOAs) for all types of IP copyright, knowhow, patents, etc. Count multiple (identical) licenses with a value of less than EUR 500 each as one license. A license grants the right to use IP in a defined field of use or territory. An option grants the potential licensee a time period to evaluate the technology and negotiate the terms of a license. An assignment transfers all or part of the right to IP to the licensee
6	Number of startups2	A new company expressly established to develop or exploit IP or knowhow created by the university/PRO and with a formal contractual relationship for this IP or knowhow, such as a license or equity agreement. Include, but do not limit to, spinoffs established by the institution’s staff. Exclude startups that do not sign a formal agreement on developing IP or knowhow created by the institution
7	Total license revenue earned	Total income from all types of knowhow and IP (patents, copyright, designs, material transfer agreements, confidentiality agreements, plant breeders’ rights, etc.) before disbursement to the inventor or other parties. Include license issue fees, annual fees, option fees, and milestone, termination and cash-in payments. Exclude license income forwarded to other institutions than those served by the KTO or to companies

Source: Authors

¹ The definitions follow those used by the AUTM, but have been adapted for simplicity and for use in countries other than the U.S. See European Commission (2009).

² Startups include both spinoffs established by university/public research institute staff using the institution’s IP and new companies that take a license to commercialize an institution’s IP, but do not include its staff.

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.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.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.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 (Arundel et al. 2013).

2.6.1 Supplementary Metrics from KTOs

In addition to the basic metrics covered in Table 2.5, KTO surveys can provide a variety of supplementary indicators of relevance to licensing. Table 2.6 lists supplementary indicators and their relevance to policy. The list is limited to indicators of value for benchmarking performance, the development of policies to support knowledge transfer and the ability of KTOs to efficiently manage patent portfolios.

Table 2.6 Supplementary metrics from KTO surveys¹

	Statistic	Policy use
Supplementary metrics for patents
1	Domestic patent applications or grants	These can be “entry-level” patents with limited commercial application, particularly if domestic patenting costs and/or the bar for a patent are low. A continuing high share of domestic patents out of total patents over time could indicate low commercialization potential or that too many low-value inventions are patented
2	Foreign patent applications or grants (USPTO, EPO, PCT, etc.)	Foreign patents in large markets such as the European Union or the U.S. indicate high commercialization potential. An increase in the share of foreign patents out of all patents indicates an improvement in inventive capabilities/commercialization opportunities over time
3	Number of granted patents in the current portfolio that are valid (patent renewal fees have been paid)	Combined with the next indicator, data on the size of the patent portfolio can be used to determine the share of patents that have ever been patented. This should increase over time as the KTO gains greater experience
Supplementary metrics for licensing
4	Number of patents in the current portfolio that have ever been licensed	The share should increase over time. A stable or declining share could indicate that the KTO is applying for a patent for too many invention disclosures
5	Licenses by licensee type: startups, SMEs, regional firms, etc.	The share of licenses to regional firms (SMEs or startups) is of interest if the government has a policy of encouraging local development. The disadvantage is that focusing on regional IP partners can reduce license revenue2
6	Exclusive and nonexclusive licenses	Nonexclusive licenses are income earners for universities/public research institutes, but are not necessary (no IP is required) if the policy goal is to get as many firms as possible to take up the knowledge covered by the license
7	Licenses by type of IP (patents, knowhow, copyright, etc.)	Patentability is limited to specific types of knowledge, with other types of IP required for other types of knowledge. Data on other types of IP used in licensing can identify if the license portfolio is commensurate with the types of knowledge produced by the university/public research institute
8	Licenses by technology field (software, biomedical, nanotechnology, etc.)	Specific technology fields can dominate licensing and license revenue (in Europe, it is the biotechnology/medical fields). Good benchmarking across universities and public research institutes should therefore be based on comparing metrics by technology field to compare like with like. Licensing by field can also be an indicator for decisions on research investments
Supplementary revenue indicators
9	License revenue from running royalties/sales of products based on university/public research institute IP	Measures of commercialization of knowledge produced by universities/public research institutes
Supplementary revenue indicators
10	Startups that have commercialized university/public research institute IP	Measure of commercialization of knowledge produced by universities/public research institutes
11	Startup sales of products from university/public research institute IP, employment in startups with such sales	Measure of commercialization of knowledge produced by universities/public research institutes

Source: Authors

¹ For brevity, this table does not include foreign research agreements or licenses.

² Belenzon and Schankerman (2009) find that universities with strong local development objectives generate less license income but have more licenses to local startups.

A 2009 review of KTO metrics in Europe and the U.S. found that only two of the supplementary metrics listed in Table 2.6 were collected in most countries: the number of valid patents in the patent portfolio (item 3) and licenses by firm size (item 5). Data on exclusive and nonexclusive licenses were available only for the U.S. and Canada via the AUTM survey and in a Swiss survey (European Commission 2009), but questions on exclusive and nonexclusive patents have been included in later European surveys (Arundel et al. 2013).

Table 2.6 includes counts for PCT patent applications. These can be filed instead of a national application and lead to patent protection in up to 140 countries. The PCT is usually only used when the applicant wishes to acquire a patent in one or more nondomestic countries. In most countries, its use therefore indicates a higher-quality patent with good commercial potential.

Standardized Indicators

The metrics in Tables 2.5 and 2.6 should be standardized for both internal and international comparisons. For instance, combining the number of current patents that have been licensed with the total number of valid patents in the university/public research institute’s patent portfolio produces an indicator for the share of licensed patents. Many of the metrics can be standardized by calculating the rate per 1,000 academic staff or per USD 1 million in purchasing power parities for research expenditures.¹⁴ Table 2.7 describes these two standardization variables.

Table 2.7 Variables for standardizing knowledge transfer office (KTO) metrics

	Statistic	Policy use
1	Total number of academic staff at a public research organizations active in fields with a potential for commercialization	All basic metrics and most of the supplementary metrics can be standardized per 1,000 academic staff. Standardization per 1,000 academics is less relevant for the supplementary licensing metrics
2	Total research expenditures in fields with a potential for commercialization	As above, but this information is necessary to compute standardized metrics for comparison with the AUTM surveys for the U.S. and is also required to calculate the license income share of total research expenditures

Collecting data on research expenditures is necessary in order to compare results with the U.S., as the AUTM does not collect data on the number of academics. Research expenditures are influenced by differences in purchasing power parities (PPP) in different countries. It is a simple matter to use PPP currency conversions, but PPPs are calculated for all economic costs, not just for research costs.

For international comparisons other than with the U.S., there are advantages in using the number of academics rather than research expenditures to produce standardized indicators. The number of academic staff in full-time equivalents (FTEs) is possibly more comparable across studies. National differences per 1,000 academics can also be a useful (albeit not an ideal) indicator of academic performance. An alternative is to standardize by the number of peer-reviewed publications (or a quality-adjusted publication measure) per 1,000 academics. Publication counts are positively correlated with patent applications (Van Looy et al. 2011; Berbegal-Mirabent and Sabate 2015).

Metrics for the Characteristics of the KTO and its Institution

For econometric research, it is important to collect control variables on the characteristics of the KTO itself and the institution to which it is responsible. Relevant KTO variables include its age and number of staff and, if possible, the area of expertise of KTO staff and the KTO budget. The KTO’s age is particularly important to obtain because it has a significant effect on many knowledge transfer outcomes, due to a positive relationship between KTO age and institutional experience and knowledge transfer activities (Friedman and Silberman 2003; Conti and Gaule 2011; Berbegal-Mirabent and Sabate 2015).

Data should also be collected on several characteristics of the public research institutes and universities that can influence knowledge transfer activities, including:

• the location of the institution in a dynamic region near innovative firms, venture capital, etc.;

• the size and type of the institution: private universities with a commercial orientation can be more active than public universities;

• the portfolio of disciplines, some of which are more prone to knowledge transfer, such as biomedical research;

• the research quality of the institution, its reputation and network; and

• the extent of existing collaboration between the institution and firms.

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.

Comment 2.1

Bhaven Sampat

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. Hvide 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 (Cohen 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., Bryan 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 (Eisenberg 1996; Mowery 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 (Eisenberg 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 (Mowery 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 (Ayres 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.

Comment 2.2

Ragan Robertson

With the deluge of data generated daily by a knowledge transfer office (KTO), strategic decisions and operational functions rely on metrics to identify areas of improvement or focus. Given the complexity of tasks managed by a KTO, determining what metrics are measured is critical. Coming from an academic background, I’ve been taught to ask “what is the goal?” or “what is the question?” before setting forth and measuring something. Asking these questions first helps determine the variables being measured, as well as ensuring that the measurers and those who will review the metrics are all in agreement as to what is being asked and answered. When focusing on measuring the functions of a KTO, I ask questions such as “what can be measured?,” “why measure?,” and “how does a metric affect other metrics?” Addressing these questions typically results in identifying the core functions of a KTO and focusing on the counts of these actions, such as licenses executed and patent applications filed. The AUTM Licensing Activity Survey is an excellent source of some of these measurements, as are the annual reports produced by KTOs. Some of these metrics are also highlighted in Table 2.5 in this chapter. Accumulation of broadly applicable data in a centrally accessible database can help facilitate these goals. This also allows for KTOs to compare their metrics with those of their peers to establish benchmarks. Another benefit of creating data as a shareable resource is the ability to share data with other interested parties, including policymakers and academics. In this way, this practice can further benefit the knowledge transfer field as a whole in addition to the office itself.

As data become more available, and our economies become more complex, KTOs are being asked to do more and more functions. One reason is that the KTO acts as a node, a nexus where industry connects to research, where intellectual property connects to contracts, where business development connects to academic pursuits. These connections in and of themselves lead to additional metrics that can be measured. How many meetings with industry occurred in the last quarter? How many different patent application families were put under an exclusive license this past year? How many sponsored research agreements were executed with licensees? As KTOs function in more roles at the interface of academics and business, more measurable data are generated and can be analyzed. However, as the roles of our offices expand, we must ensure that the data we are generating and measuring can lead to potential actions and not just to the act of obtaining data. We must always ask why we are measuring something and what we can do to change it for the better.

Naturally, due to the nature of the knowledge managed by KTOs, offices are becoming more involved within the entrepreneurship ecosystem. Subdivisions within KTOs, and even entire new departments, are being formed to address this growing role. The function as a nexus causes KTOs to be well-positioned to have an incredible impact in this ecosystem. By having the connections to industry, as well as access to new research being conducted, KTOs can identify the opportunities where new research results can lead to the formation of a new company. The ultimate result is that KTOs can have a significant impact on the economy, especially at the local level. New companies formed, new jobs created, new revenue generated, and new taxes paid are some of the more easily identifiable benefits to economic output that can be sourced from the work performed by a KTO. This additional role for KTOs within the entrepreneurship ecosystem can also lead to unintended benefits, such as new institutional donors, or diversification of industry within a local ecosystem.

However, we do have to be careful to assign economic output to KTOs accurately and avoid generalization of data or misattribution of revenue. Skeptics of the industry have focused on return on investment (ROI), typically syncing research dollars sourced from a governmental agency and the licensing revenue received directly by a KTO. While this connection is an oversimplification, expanding to the other extreme of attributing all economic benefit generated by external entities connected to a KTO is also misleading. In addition to drawing conclusions based on overgeneralization of data, KTOs must also be wary of “paralysis of analysis” in which the goal is to gather more data without making a decision, or the confusion that results from an overabundance of data from multiple sources. This speaks directly to the question as to what the true impact is downstream of a KTO’s activity. This harks back to asking ourselves the question of why we are measuring something and what we can do to change it for the better.

This chapter does an excellent job of identifying key metrics that should be measured by KTOs, along with the reasons why they are measured and the potential economic impact. While these metrics are clear and should be implemented by KTOs, the industry still lacks metrics to measure several important functions of KTOs. These include metrics to measure the effect of a KTO’s work on societal impacts. As identified in this chapter, these impacts are very difficult to measure, typically relying on case studies such as those found in the AUTM Better World Project. Case studies, unfortunately, can be overly specific and their conclusions can be difficult to apply to general practice. However, there are metrics that could be used to address this impact. For example, for the past few years, AUTM has been collecting data regarding women inventors, specifically how many disclosures include a woman and how many new patent applications include a woman. What actions can or should be taken related to these data are only now being developed, but it is a start toward how to address a significant societal impact. Expanding on the theme of inclusion, metrics could also be collected related to race or ethnicity. KTOs may already have some of these data, as we must report citizenship within patent applications, but they have yet to be utilized. These data have the potential to have a far-reaching impact outside of the knowledge transfer or academic spheres, and it will be interesting to see what comes of them.

As identified, metrics are a key necessity for any KTO to inform strategic decisions and operational functions. Metrics need not only include the core functions, such as patent application filings and licenses executed, but also other important economic drivers, such as startups formed and investments raised. However, no matter what metrics a KTO measures, they are numbers for numbers’ sake if the metrics are not aligned with a KTO’s goals. Metrics must be actionable – how can a team affect them and what does that effect mean? By being clear and transparent as to how a metric informs and achieves its goals, a KTO is on the path to making an economic and societal impact.

Comment 2.3

Rosemary Wolson

Senior Intellectual Property Manager, CSIR

The chapter examines various modes of knowledge transfer from universities and public research institutes to industry, together with the policies that support knowledge transfer, in order to develop a conceptual framework for comparative country studies and identify relevant and useful metrics for assessing the economic impact of this activity.

While some common trends can be seen in policy and legislation around the world, a variety of policy instruments and methods are employed in developing and implementing relevant legislation and policy. It is recognized that knowledge transfer takes place through both formal and informal channels, and in different countries and organizations, the predominance and importance of each may differ. The prevailing mix in a particular context must be taken into account in making policy (at both national and institutional levels), to avoid inadvertent consequences of policies aimed at promoting one channel negatively impacting on others that may, in fact, be of greater importance in the relevant environment.

As a starting point, this calls for a comprehensive understanding of the ecosystem and its various actors and institutions. Disruptions to the status quo can yield both positive and negative effects and the possible impact of both must be considered to ensure that potential benefits outweigh potential costs. Assumptions must be validated so that existing strengths can be built on, gaps can be filled, and, ultimately, that fit-for-purpose policy can be developed.

Policy priorities should be clearly articulated. Different policies may be needed to promote different objectives, rather than trying to achieve too many outcomes by means of a single policy, especially when such outcomes might not support one another. Where different bodies are responsible for making policy for different knowledge transfer channels, effective coordination between them becomes critical to ensure that conflict does not arise. If tradeoffs are required to achieve an optimal balance, these must be identified and agreed.

It is always useful to draw from experiences and best practice elsewhere when developing policy. Understanding what has not worked well, and why, is arguably as important as examining successful interventions. At the same time, borrowing uncritically without making relevant adaptations for a particular country’s own circumstances is likely to lead to suboptimal results. This is perhaps especially true where policies and practices from developed countries are applied in low and middle income countries characterized by less developed innovation ecosystems and an industry sector with inherently less absorptive capacity for new innovations.

New policies must be sufficiently flexible to accommodate responses necessary to correct for unintended consequences that may be experienced. The choice of policy instrument should therefore be carefully considered. At national or regional level, legislation creates certainty and demands compliance, but making amendments becomes onerous. By thesame token, if policy is implemented via nonlegislated policy documents, frameworks, codes of good practice, or guidelines, these can be adapted with greater agility.

Policy should, in the first instance, aim to create an enabling environment that allows knowledge transfer to thrive, by providing support and incentives. Overly prescriptive requirements or those introducing undue administrative burdens carry transaction costs that can detract from productive knowledge transfer activity and disincentivize compliance. One-size-fits-all policies may lead to certain channels of knowledge transfer being neglected.

Once policies are put in place, it becomes critical to evaluate their implementation objectively on a regular basis, to ensure that they are functioning effectively. Measuring performance allows comparisons to be made, trends to be identified and the achievement of targets and goals to be assessed. This yields information on what is working as intended and what needs to be improved or changed, and can be used to inform adjustments in policy and practices to achieve greater impact.

The chapter provides an instructive examination of a range of knowledge transfer metrics that are and, further, that can be collected, together with explanations of the reasons for and value of gathering different types of metric, both basic and supplementary. It also emphasizes the importance of using a variety of data sources to obtain a balanced view. While surveys from knowledge transfer offices are perhaps the most common source of data in this regard, they should be supplemented with data from other stakeholders, such as industry and researchers, particularly for knowledge transfer channels other than IP licensing. Recommendations are made in respect of which metrics should be regularly collected, from where, and how frequently.

Designing, collecting, and reporting a suitable set of metrics is not, however, a trivial exercise. In doing so, it is worth recalling William Bruce Cameron’s observation that “not everything that can be counted counts, and not everything that counts can be counted.”¹ The chapter notes that many of the available data relate to the knowledge transfer channel of IP licensing (in high- and middle-income countries). This can be attributed at least in part to the fact that many of the activities associated with this channel provide several easily quantifiable indicators along the value chain, such as invention disclosures, patent applications, issued patents, licenses executed, and license fees earned. Most of these are, however, indicators of inputs into or progress toward commercialization rather than of economic impact or social benefits, which still remain difficult to measure directly and accurately (since the outcomes concerned are usually not solely attributable to knowledge transfer, but also to a range of other factors and influences).

Metrics can serve as significant drivers of behavior, particularly when linked to individual or institutional performance evaluation frameworks. Overemphasis on input metrics is likely to lead to increased activity in these areas, but will not always result in improved outputs or outcomes, unless appropriate ecosystem support and complementary incentives are in place.

“Vanity” metrics, which may superficially tell a positive story but fail to provide practical information on performance, should be avoided in favor of actionable metrics that can be used as a basis for implementing improvements to policy and practice.

The more comprehensive a set of metrics is the greater its value. But in selecting which metrics to capture, attention must be paid to the ease of acquiring and accessing the requested data by the survey respondents. If the data requirements are too ambitious, there is a risk of lower response rates, and/or supply of incomplete or inaccurate data.

Where metrics are used specifically for benchmarking purposes, data must be appropriately normalized (standardized) to ensure that one is comparing “like with like.”

Where a set of metrics focuses on a particular knowledge transfer channel, institutions or regions that pursue other channels more actively might be reluctant to participate, fearing that their performance will not reflect favorably when measured against that of other institutions/regions.

Each of these challenges must be acknowledged and tackled. Nonetheless, the benefits of a robust set of metrics generated on a regular basis cannot be denied. This is achievable with buy-in from all key stakeholders who recognize the value this can bring for improving performance and enhancing impact in their respective spheres, whether as policymakers or practitioners.

3 Measuring Global Patenting of Universities and Public Research Institutes

Juan Yang , Intan Hamdan-Livramento , Bruno Le Feuvre , Sacha Wunsch-Vincent , and Hao Zhou

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 (Perkmann 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 (Mowery et al. 2004; Rosenberg and Steinmueller 2013; WIPO 2015). Academic patenting has also been used by university researchers since the late 1800s (Mercelis 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 (Graff 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 (Wright et al. 2008; Van Looy 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 (So et al. 2008). As a by-product, this type of policy provides an avenue for generating income for universities (Geuna 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 (Rothaermel 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.

3.2.1 What Do Patent Data Tell Us?

There are many limitations to using patenting data to track the performance of public research organizations. First, patent data say relatively little about whether the patented inventions actually result in innovations. In particular, patented inventions from universities tend to be further from commercialization potential than those in the private sector (Henderson et al. 1998; Sterckx 2011). In this sense, patent data provide a relatively imperfect measure of technological activity.¹

Second, patents are used by universities and public research institutes in a somewhat different way from private sector patents. In the private sector, patents are generally used to appropriate the firms’ returns on investing in innovation.² Universities and public research institutes, by contrast, do not directly commercialize their inventions and instead rely on patents to attract industry counterparts. Thus, patents are used as a signal to indicate the value of the protected invention.

Third, a significant share of inventions originating from research performed at universities or public research institutes – university-invented patents – are not patented under the organization’s name. Depending on their employment contract and applicable laws, academics and researchers working in these organizations may be able to file the patent under their names and may later assign the rights to universities. Others may prefer to file under their own names to start their own companies later. A small percentage of university faculty assign the university invention under firm names only and not under the university’s name, contrary to university policies (Thursby et al. 2009).

In our methodology, applicants are classified according to their names only, without considering their employment relationship or address. Therefore, where a natural person is the applicant filing on behalf of an educational organization, that application would not be classified as belonging to a university. Instead, it is imperative that the first applicant is the university or public research institutes itself in order to be categorized as a university or public research institutes patent.

Last, many methods of capturing academic patenting are based on keyword searches and a list of university names. Lesser-known universities or public research institutes, or even those who file their patent applications using different names, may not be captured. As a result, a sizable share of patents derived from public research is underestimated.

3.2.2 How Does This Apply to Public Research Organizations?

Academic patenting is not new (Mercelis et al. 2017). For a long while – before laws such as the Bayh-Dole Act came to pass – academics enjoyed the privilege of having the first commercial rights over their inventions (Kelly 2016). Some filed for patents on their research work to ensure control over how their work was used, others to build their reputations.

Before the Bayh-Dole Act in the U.S., there was a low level of knowledge transfer from universities to industry; only 5 percent of government-owned university patents were commercialized (Schacht 2006). One of the main barriers to the transfer was the issue of relinquishing ownership rights. First, there were approximately twenty-six different agency policies governing how results of federally funded research and development (R&D) would be used. Second, licensing policies in place did not provide the appropriate incentive mechanisms to facilitate knowledge transfer.

Changes in the rules governing university patenting have had an impact on the academic patenting culture. First, there has been a general increase in university patenting (Geuna and Rossi 2011; Thursby and Thursby 2011). Second, academic patenting has increased the probability that researchers and professors will start their own companies (Aldridge and Audretsch 2011; Kenney and Patton 2011). Third, while publishing research work in journals continues to be important, there are noticeable delays in publication (Blumenthal et al. 1997). These changes and more have led many to consider universities now as entrepreneurs, not merely knowledge generators (Grimaldi et al. 2011).

3.3 How to Measure Academic Patenting?

3.3.1 Data Source

The most comprehensive patent data available today are the WIPO PCT and EPO PATSTAT (April 2016 edition) data sets. We use these two data sets complementarily because they are able to capture patenting activities worldwide. The difference between them is that PATSTAT compiles national patent data from many countries while the PCT captures patents filed through the international PCT system.

The advantage of using the PCT database is that the information is complete and comparable across countries. Patent applicants who wish to file for patent protection in multiple jurisdictions may use the simplified PCT patent filing system. An applicant may deposit their international patent application directly with WIPO either online or by mailing it in, or through national IP offices that send the application to WIPO later. All PCT member countries are able to use this simplified patent filing system.

However, PCT filing is only a subset of all patenting activities. First, applicants who decide to use the PCT route do so because they are interested in filing in several national patent offices and the PCT system allows for a simplified application process (see Box 3.1). It is generally accepted that patented inventions that have been filed at more than one large IP office are of higher value than those that are filed domestically (Dernis and Khan 2004). In this regard, patent applications under the PCT may be considered of higher value due to the potential to acquire patent rights in multiple jurisdictions.

Second, applicants may use the PCT filing system as a business strategy. Universities and public research institutes that choose the PCT system may do so because of the thirty-month transition time between filing for a patent and national phase entry. Anecdotal evidence suggests that some KTOs in universities prefer using the PCT system because it gives them additional time to find commercialization partners for their university inventions. Other applicants may use the PCT system to assess the likelihood of their invention being patentable (Guellec and van Pottelsberghe de la Potterie 2007).

Third, PCT filing may also reflect universities’ and public research institutes’ stronger or weaker propensity to file abroad.

These factors point to the drawback of using PCT data – that they may underrepresent the total universe of academic patenting, and may merely reflect the strategic patent filing behavior of different universities and public research institutes.

Moreover, there are also cost considerations in filing through national IP offices or through the PCT system. The PCT system is a rational filing method if the applicant intends to file in multiple jurisdictions; if not, the costs of application may outweigh the benefits.

The EPO’s PATSTAT database, by contrast, allows us to examine a larger set of university and public research institute patenting activities. The PATSTAT data set comprises patent data from different national IP offices that share their data with the EPO, making it easier to capture universities and public research institutes that choose to apply in a single jurisdiction.

But unlike the PCT filing data, the PATSTAT data set is not always complete. Many IP offices in high-income countries provide their patent data to the EPO; the same cannot be said for less-developed economies. Missing data for some offices and years makes the use of this database to run cross-country analysis challenging.

One way to check PATSTAT country and year coverage is to compare the total counts of patents listed in PATSTAT with information collected by WIPO. WIPO conducts an annual survey of national and regional patent office data on patent applications filed. PATSTAT includes only data on published patent applications. A small discrepancy between the two groups – filed versus published – is to be expected: the former is always larger, since some applications are withdrawn before publication, and there is a time lag between filing and publication. If the difference between the numbers as reported to WIPO and PATSTAT for a particular national IP office is small then we can consider PATSTAT coverage of that country reliable; if the difference is large then the data should be analyzed with caution.

Table 3.1 provides a quick overview of national patent data coverage at PATSTAT for the six countries studied in this book. It compares the number of patent filings at the different jurisdictions as reported to WIPO with the number provided by PATSTAT. For South Africa and the Republic of Korea, there are significant discrepancies between the total number of patents filed as reported to WIPO and PATSTAT. There are many possible explanations for this. National events or even changes in legislation in a country may be reflected in its reported IP data.

Table 3.1 Comparison of PATSTAT national patent data coverage for featured countries

National IP office	In PATSTAT	PATSTAT coverage	Incomplete information
Brazil	Yes	Good	2011–14
China	Yes	Good	1984
Germany	Yes	Good
South Africa	Yes	Mostly good, patchy for some	1986–9, 2008–10, 2013–14
Republic of Korea	Yes	Mostly good, patchy for some	1985–97
United Kingdom	Yes	Good	1980–82

Source: WIPO Department for Economics and Data Analytics Internal Documents, September 2017

3.3.2 Identifying Public Research Organizations

Identifying universities and public research institutes using patent data is not straightforward. Due to the differences in the patent data contained in the PCT and PATSTAT databases, we employ similar methods but with a few important variations to capture academic patenting activities.

First, patent documents do not contain standardized information on the applicant type, so we rely on the information contained in the applicant’s name or address in developing search algorithms to identify university and public research institute patents.

Using the PCT database, we search the names of applicants or their addresses as recorded in patent documents, and determine whether the applicant is a university, public research institute, company, or individual using certain words, for example, “university,” “college,” “school,” “government,” and “ministry.” We perform this search in various languages to make sure that we also capture organizations in non-English-speaking countries. Moreover, we have a list of universities and public research institutes that we use specifically in the context of PCT filings to help us identify academic patents.

The PATSTAT database provides a table which categorizes applicant types by the following four categories: individuals, private business firms, universities and higher education organizations, and government agencies.³ This applicant classification was developed by the Catholic University of Leuven in Belgium, which employed a similar search strategy to ours.⁴ We use this categorization to target the subcategory of patent applicants that have been classified as “universities and higher education institutions.”

Second, name-cleaning is tedious. Applicant names provided in the PCT and PATSTAT are neither standardized nor harmonized, making it challenging to identify universities and public research institutes by either keywords or names. In addition, applicant names and addresses may be in languages other than English and may be written in non-Latin characters.

Ensuring that the list of university and public research institute names captured is representative of the different languages as well as non-Latin characters would require additional lists of keywords or similar name matches. In this respect, the PCT database provides an advantage over PATSTAT. PCT filing requires applicants to provide their names in a standardized English version as well as in the nine other languages accepted.⁵ The applicants’ names and addresses have to be indicated in Latin characters, either as transliteration or translation into English.⁶

The national IP data provided for PATSTAT, however, can be in any language, including exotic languages, and the applicants’ names and addresses may be listed in non-Latin characters. Accordingly, we employ a WIPO-created list of university and public research institute names and the associated keywords to capture academic applicants that may have been unintentionally omitted by PATSTAT through its applicant type table.

This list was created through direct contact with government officials, and verified by consulting government websites as well as university and public research institute directories. It contains the names of universities in fifty-four countries and public research institutes in thirty-eight countries.⁷ We mined the list to identify keywords that would help us tag universities and public research institutes in the different languages. We further added to this list the top 200 publishing organizations from sixty-two different countries that we have established using Scopus, a database containing citations and abstracts for scientific journal articles.⁸ And last, we use the Scimago Institutions Rankings World Report (2010) to include the top publishing organizations in the world – 2,833 in total.

Third, the name-matching processes for academic organizations under PCT and PATSTAT differ due to the volume of data to process. At the last count, PATSTAT covers over 100 million patent documents while the PCT covers 3.5 million patent documents.⁹

For the PCT, once we have identified patents from universities and public research institutes, we manually match all the names that seem similar. In the case of PATSTAT, we focus on the top filers, assign a similarity value based on the similarities of the names, and try to match them. So for example, if we wish to identify the top 100 academic organizations we look at the top 300 filers, find those that are similar and then match them manually.

Fourth, the decision on patent family definition (see Box 3.1) should be tailored to the research question, especially in the case of PATSTAT.¹⁰ Since PATSTAT provides all available national patent data collection, universities and public research institutes that have filed for patents in multiple jurisdictions for one invention would need to be accounted for so that we do not double-count those filings. This is not the case for PCT, as it is just a patent-filing method.

Box 3.1 Defining patent family

A patent family is a set of interrelated patent applications filed in one or more offices to protect the same invention. The patent applications in a family are interlinked by one or more of: priority claim, PCT national phase entry, continuation, continuation-in-part, internal priority, and addition or division.

A special subset comprises foreign-oriented patent families – those patent families that have at least one filing office different from the office of the applicant’s country of origin. Some foreign-related patent families include only one filing office because applicants may choose to file only with a foreign office. For example, if a Canadian applicant files a patent application directly with the USPTO without having previously filed with the patent office of Canada, that patent family will constitute a foreign-oriented patent family with just one office.

For the purposes of this chapter, we define a patent family based on the earliest filing and where all other filings claim this first filing as a priority. In particular, we focus on patent families that are associated with patent applications with inventions and exclude those associated with utility model applications. The benefit of this patent family definition is that it enables us to track where the invention was first filed and where the applicant later sought protection for that particular invention (Martínez 2011).

And last, assigning the origin of the university and public research institute patent is usually done based on the residence – not the nationality – of the first applicant. In the case of PCT data, it is simply the first applicant’s residence as noted in the PCT filing document. For PATSTAT, it would be the residence of the first applicant of the first filing for that patent family.

3.3.3 Quality Checks

One of the main issues in identifying patent activities by public research organizations is to ensure that we have correctly captured applicants who fall within this category.

When carrying out the strategy of identifying public research organizations, researchers need to ask themselves which problem is worse: including applicants who do not fall under the category of public research organizations (false positive) or excluding those applicants who do fall under the category (false negative)?

Several quality checks have been performed, especially on the method used to extract university and public research institute patents from the PATSTAT database. Two issues emerged: first, whether the data compiled by PATSTAT has good country and time coverage; and, second, whether the search method employed performed well in identifying the academic organizations.

The first question can be addressed by comparing PATSTAT data on aggregate applications per year per country of origin to aggregate numbers reported to WIPO by national and regional patent offices, as we did to produce Table 3.1 above.

To verify how well the search method identifies organizations, we compare the results obtained with government reports for selected countries wherever available.

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.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.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.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).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 (Perkmann 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.