Expectations for the University in the Knowledge-Based Economy

Expectations for the University in the Knowledge-Based Economy ˜ O, MANUEL V. HEITOR, and PEDRO M. OLIVEIRA PEDRO CONCEIC¸A

ABSTRACT As there is an increasing perception of the importance of knowledge creation and distribution for economic prosperity, what kind of role should universities play? Which public policies are more effective in promoting this role? These are the fundamental questions this article addresses. The objective is to understand the expectations for the universities in developed countries under a public policy perspective. More specifically, we discuss public policies that can promote, and those that can hinder, a positive and cumulative role of universities in the knowledge-based economies. The article systematizes the economic relevance of knowledge using recent advancements in the so-called new growth theories. Some empirical manifestations of the increasing importance of knowledge are analyzed. We briefly discuss the mission of the university as it is almost universally perceived today. Universities have been viewed as producers of new codified knowledge through research and as providers of human capital through high level education. The evolutionary trend of these functions, in which the university’s research importance to promote the learning ability of graduates has been enhanced, is discussed within the context of the knowledge-based societies. The analysis is presented in terms of the impact that public policy, and especially public funding, may have in fostering or hindering the positive contribution of universities for economic prosperity. The fundamental criterion, we argue, is the preservation of the institutional integrity of the university.  1998 Elsevier Science Inc.

1. Introduction The contemporary university is faced with a two-fold challenge: society presents it with new and growing demands, while at the same time the state applies increasingly restrictive policies to the funding of its activities. The combination of these two factors is reflected in a growing diversity of funding sources and mechanisms [1]. Strongly marked by the rapid expansion of the 1960s and the crisis of the 1970s, universities are seeking creative responses to the new demands of society by reforming the structure and organization of the activities inherited from this period. Investing in closer links with the community, the university sets itself the task of examining the quantitative and qualitative needs of its activities and of finding new ways to exploit ˜ O is an Assistant at Instituto Superior Te´cnico in Lisbon, Portugal, and a Research PEDRO CONCEIC¸A Assistant of the IC2 Institute, “Innovation Creativity and Capital” of the Universtiy of Texas at Austin. MANUEL V. HEITOR is Deputy President at Instituto Te´cnico in Lisbon, Portugal. PEDRO M. OLIVEIRA is the coordinator of the Studies and Planning Office of the Instituto Superior Te´cnico in Lisbon, Portugal. Addess correspondence to Dr. P. Conceic¸a˜o, IC2 Institute, University of Texas at Austin, 2815 San Gabriel, Austin, TX 78705.

Technological Forecasting and Social Change 58, 203–214 (1998)  1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

0040-1625/98/$19.00 PII S0040-1625(98)00018-3

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204 TABLE 1 Evolution of the Distribution of Employment Economies/year Advanced industrial 1870 1950 1995 Middle-income 1995 Low-income 1995

Agriculture (%)

Manufacturing (%)

Services (%)

49 25 5

27 36 30

24 39 64

30

28

42

62

15

23

Source: World Bank [24].

its scientific and technological potential [2], while striving to maintain its effective autonomy [3]. These efforts have led to the recognition that beyond the university’s traditional roles in education and research, a wide range of other activities, usually grouped together under the heading of “provision of services” or “links to society,” are now part of the university’s mission [4]. Despite the increasing diversity of funding sources, in most university systems the state remains the main or sole funding source [5]. The growing pressure to reduce public spending, the emergence of demands arising from other social policies, and the rethinking of the relationship between state and public bodies in general, all put considerable pressure on public funding of universities. At the same time, in a society in which knowledge is assuming growing importance, the state seeks creative ways of funding the activities and institutions that help to strengthen this factor. Among these institutions, the university occupies a prominent place [6]. These factors, in which the function of the university as an institution for creating and distributing knowledge takes on new importance, while at the same time the means of funding these institutions are being questioned, shape the way in which universities relate to the contemporary societies. University funding thus assumes crucial importance in the context of public policies for the development of universities and of society, especially at a time when knowledge is becoming predominant in the economy. Nonetheless, it is worthwhile to note that the importance of knowledge for development has not been uniform across the world [7]. One of the most important manifestations of the increasing economic importance of knowledge-based activities is the tendency of the labor force to move to the service sectors [8]. Table 1 illustrates this tendency for the advanced industrial countries. However, the weight of services for the less developed countries is still very low. The 1995 levels of employment in the services for the middle- and low-income countries correspond to the levels exhibited in the developed countries, respectively, in the 1950s and in the late 19th century. This reality has led Salomon [9] to argue about the oneway interaction between two civilizations: one depending on the creation of knowledge, the other accepting passively the knowledge created in the first. The emerging patterns of globalization and the increasing importance of sustainability, also a global issue, may lead to a change in the rationale for creating knowledge, something that Salomon classifies as compassionate scientific curiosity. The practical implication of the issues raised above is that we face different problems, depending on whether we are in developing or developed countries. The problems

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faced by lower-income countries are far more severe [8]. Developing economies need to create employment, to raise the national income, and to drive population growth down. On the other hand, developed countries are struggling with jobless growth and rising levels of unemployment, along with slowdowns in the state of productivity growth. In the United States, although unemployment levels are low compared to Western Europe, income inequality has been a growing concern. It is within this overall context that we discuss the expected role of the university in terms of its traditional functions of teaching and research. We discuss, in Section 2, the economic significance of knowledge. We make use of the recent advances in the new growth theories to systematize the way knowledge contributes to economic development, and why and how this contribution is different from the impact of material assets. In Section 3 we square these insights with the outputs of the university that we consider to be mainly two different types of knowledge. In Section 4 we discuss the rules by which these outputs are produced, and the possible impact of public policies and especially funding mechanisms. Finally, we conclude in Section 5, arguing that the fundamental principle that should rule public policies toward universities in the age of the knowledge-based societies is the preservation of the university’s institutional integrity.

2. The Economic Relevance of Knowledge The aim of this section is to provide a conceptual basis for the economic perspectives on knowledge. After providing a brief description on the neoclassical perspective on economic growth, we discuss in some detail the main characteristics of the new growth theories. These theories emphasize the impact on economic growth of knowledge and learning. With this theoretical framework in mind, we discuss some of the empirical manifestations of the knowledge-based economies. 2.1. NEOCLASSICAL MODEL OF ECONOMIC GROWTH

Solow [10, 11] introduced the basis for what came to be regarded as the pure neoclassical model of growth. Solow’s aim was to distinguish the effects on growth of conventional inputs (aggregate capital and labor) from the impact of exogenous technological change. He began with pure neoclassical assumptions: capital (K) and labor (L) constitute the inputs that firms transform through a technology (represented by a production function) at time t; inputs and outputs are exchanged between firms and households in purely competitive markets where relative equilibrium prices are determined by market clearing; firms maximize profits and households maximize utility. An aggregation to the economy’s production functions and inputs and outputs leads to the economy’s production function: Y(t) 5 F(K(t),L(t),t). This aggregate production function requires some additional properties, namely constant returns to scale, positive, but diminishing, marginal returns to each of the inputs, and asymptotic and zero-limit properties known as the Inadda conditions. The assumptions of the neoclassical production function entail that a steady-state rate of growth is attained, due to the hypothesis of diminishing marginal returns to capital and labor. At this equilibrium, net investment (net flow of capital) equals zero, since gross investment just covers the depreciation of capital, and growth is determined only by the exogenous rate of technological change (and by population growth, that we will ignore). This exogenous rate of technological change is modeled as a parameter.

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The major accomplishment was to differentiate between conventional inputs (capital and labor) and technology, clearly a different kind of input. A further accomplishment occurred when capital was divided between physical and human capital. However, in the neoclassical tradition, technology was regarded as a public good, and human capital as a pure private good. Technology was viewed as if it was a radio wave, freely available to all with the conditions to receive it. And the conditions were related to the level of education (the potential for increasing the amount of human capital) and the savings rate (the potential for increasing the amount of physical capital) [12, 13]. Our inheritance from the neoclassical models can be summarized in three key points: (1) knowledge (of which technology and human capital are part) is either a public good or a private good; (2) technology is exogenous, determined outside the economic context; and (3) growth exhibits diminishing returns. 2.2. NEW GROWTH THEORIES

New growth theory blossomed after Romer [14] used the ideas put forward by Arrow [15] concerning the economic implications of learning-by-doing. Learning-bydoing reflects the fact that an increase in the utilization of capital leads not only to a scale effect, but also to an increase in the knowledge used in production because of additional experience. More formally, there is a positive effect of experience on productivity that can be formalized as knowledge creation as a side product of investment or capital accumulation. Mathematically, this can be expressed by stating that each firm exhibit a neoclassical labor augmenting production function Yi 5 F(Ki, Ai.Li) where Ai can be understood as the index of knowledge available to the firm. No assumptions are made regarding the growth rate of Ai, except that it exists and is constant in the steady state. The development of the model relies on the following two assumptions: 1. Learning-by-doing, which increases with the firm’s investment. Therefore, increases in the firm’s capital stock have a parallel increase in the firms level of knowledge Ai, that is, K·i/Ki 5 A·i/Ai. 2. Knowledge spillovers. The knowledge at each firm is a public good, meaning that knowledge spills over from one firm to the economy instantly, and any other firm can access this new knowledge at zero cost.1 It is important to note that this assumption means that all new knowledge is an unintended result of investment; it does not result from purposive actions of the firm. This assumption leads to A· 5 const. K·, that is, the increase in knowledge in one specific firm is reflected in the increase of capital of the whole economy. Combining assumptions (1) and (2) we can replace Ai by K and express the production function as Yi 5 F(Ki, K.Li). This development leads to the so-called AK model of economic growth. In this case, growth is endogenous and results exclusively from learning-by-doing. For an interesting 1 We could assume, perhaps more realistically, that the spillover effect is limited to a region or to a particular industry. The fact that we assume that knowledge spills over the entire economy might lead to predictions that exaggerate the scale effects that we will discuss later.

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Fig. 1. New growth theories.

discussion on how Nelson’s 1962 article was a precursor of Romer’s efforts, we refer the reader to the work of Solow [16]. Two other “flavors” of the new growth theories emphasize the formal institutional mechanisms in existence in our societies to accelerate the learning process, namely education and research. The models relying on education follow the seminal work of Lucas [17]. Romer [18] and Grossman and Helpman [19] are the canonical references for the models having research and innovation as the source of endogenous growth. In this context, Pasinetti [20] articulated the idea that learning is the clearest source of growth. He observed that, since man is able to learn, advancement occurs because the next society always has a better departure point than the previous. In this context, Pasinetti puts the learning ability of human beings as the ultimate key to economic growth, a perspective that is the key feature of the new growth theories, although, in Pasinetti, the sources and mechanisms of growth differ from the new growth theorists. Figure 1 illustrates the relationship between learning, growth, and the underlying factors that enhance a learning ability society wide. The main references associated with the different perspectives are also indicated. The fundamental characteristic reflected in learning is the ability to create and recreate knowledge. However, the new growth theorists’ perspective on the economic nature of knowledge is rather different from the one we inherited from the neoclassical framework. Romer [21] suggested a differentiation between human capital, a private good, and “software,” the non-rival knowledge that is codified in books, computer programs, journal articles, CDs, and the like. The fundamental characteristic of software is that it can be copied and communicated at virtually zero cost. Software can be made excludable, using intellectual property protection mechanisms, or it can remain nonexcludable. If it is made excludable, then there is a private incentive for production, such as in commercial software. If it is not made excludable, as in a scientific journal article, then it has the characteristics of a public good. Even if knowledge is excludable, yielding to a private motivation to produce, it remains non-rival. Consequently, although there may have been extremely high fixed costs to, say, develop a software package, the variable costs associated with production are virtually nil. Therefore, the value of this software package depends only on the scale of the market, and the producer benefits from increasing returns. Brian Arthur [22] explored extensively the economics of increasing returns. Besides zero variable costs, two other factors reinforce the existence of increasing returns in excludable

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knowledge: network externalities and customer lock-in. Network externalities are associated with the establishment of a standard. Customer lock-in is associated with the difficulty of users to use new products after they have learned how to use a specific one. A good illustration of the economics of increasing returns is the case of Microsoft Corporation. This company deals with computer software, a non-rival but excludable knowledge-based product. Microsoft was able to set a standard, Windows 95, and benefited from customer lock-in, as PC users were increasingly reluctant to change from the windows and pop-up menus system that they mastered.2 2.3. EMPIRICAL CHARACTERISTICS OF THE KNOWLEDGE-BASED ECONOMY

There is a general perception that today’s developed economies depend on knowledge and information in an unprecedented way. Traditional accounting and statistical reporting methods, both at the firm and the national level, make it extremely difficult to test any claims based on knowledge or intangible assets. Knowledge, as we saw, is not a traditional economic input, like physical capital or labor. Stevens [23] mentioned that the idea of a knowledge-based society remains more of a concept than a measurable entity. At the same time he makes the claim that about half of the OECD countries’ GDP is now knowledge-based. Allan Greenspan, the Federal Reserve Board chairman, said recently that the American economy’s GDP weighed today, in tons, as much as it used to 100 years ago [24]. But how can we measure these claims? So far, the evidence comes mainly from stylized facts, such as the growing incorporation of knowledge in products, the increasing value associated with software, as opposed to hardware, the growing strength of services. Let us consider each of these in turn. Manufactured products are increasingly characterized as “smart.” Take cars, for example: a typical car today has more computer-processing power than the first lunar landing-craft had in 1969, according to Wyckoff [25]. This author mentions tires that tell the driver when the pressure is too low. Also, Davis and Botkin [26] describe how Massey Ferguson, a farm-tractor manufacturer, blended information and communication technologies to provide the farmers crop management tools (these were called, of all things, knowledge-based farm tractors). This incorporation of knowledge in products had led to the increasing importance of software, in comparison with hardware. A metric mentioned by Wyckoff [25] reflects this fact: in the 1970s, 80% of the value of an IBM computer was hardware and 20% was the cost of the incorporated software; in the late 1980s, the shares were reversed, and ever since have moved in a direction where the value of software keeps increasing. Software, namely the knowledge incorporated in products, is more important not only in terms of product differentiation and value added, but also as a source of innovation. This has led to the introduction in the literaure of the idea of a reverse “product lifecycle,” which occurs when knowledge, say computer software, is developed in order to increase the differentiation of a product or to improve the efficiency of a production process. Normally, the product or the process would be in a declining phase in terms of the traditional life-cycle. But then, the computer software becomes in itself a new product or service, and is commercialized in its own right. Take the case of SABRE, developed by American Airlines to improve their internal flight scheduling process. Eventually, SABRE became one of the most successful products of the company, surpassing in terms of rate of return the traditional business of selling flights [25]. 2 Incidentally, as is well known, Microsoft was not the original innovator, but this is another story that has been analyzed thoroughly.

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Fig. 2. U.S. Employment by sector 1820-1995. Sectors: agriculture (diamonds), industry (squares), and services (triangles). Source: OECD [3].

Finally, we recall that the increasing attention to knowledge production has led to a dramatic shift in employment from the industry sector to the service sector in the last decade in most OECD countries, as documented extensively in a recent issue of the OECD Observer (1996). The evolution of the U.S. labor employment is illustrative of this tendency, as Figure 2 shows. At the beginning of the 19th century, more than 75% of the workforce was employed in agriculture. In 1995, three-quarters were employed in the much more productive services, leaving agriculture only with 3% of the total. In the next section we associate this trend with the university’s impact in the process of knowledge creation and diffusion. We derive public policy implication from this analysis in Section 4.

3. A Perspective on the Mission of the University Universities are diverse institutions across countries and even within countries. It is always a great risk to refer generically to such a rich type of organization. Higher learning occurs in many different institutional settings and is intended to serve several different ends. In the context of this article, we focus on the institutions that in the United States are referred to as research oriented graduate universities. In Europe, the term university is associated with a similar type of institution. In this context, it is consensual that universities have as their primary role to provide higher education and to develop research activities. Rosovsky [27], Lucas [28], Readings [29], and Cole et al. [30], among others, provide thorough discussions on the university’s mission in the American context, while related aspects in the European setting have been discussed by Conceic¸a˜o et al. [1]. Therefore, we will divide the universities’ activities into teaching and research. Teaching deals with the creation of human capital. In fact, most econometric studies use the formal education level as a proxy for human capital. This activity is traditionally associated with the transmission of existing knowledge from faculty to students. The value of this activity rests in the individual interpretation and use of this knowledge by the students, especially after they graduate. Blended with their personal characteristics,

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this knowledge constitutes the human capital that each person acquires after going through the university. Since this knowledge is specific to each individual, and there is a perception that employers are willing to pay a premium to reward the investment in human capital, this is a private good. In fact, actual performance by graduates has been gaining in importance over academic credentials [30]. Therefore, the formal aspect associated with having a degree is being less emphasized in comparison with the human capital that each individual gets. The main question that arises is how the university’s teaching system may maximize the enrichment of capacities in its graduates, an issue analyzed below in terms of the learning required to growth, as discussed in Section 2. Within this context, the other question that is facing the university system relates to the faculty’s role, which should again be considered in terms of the ability to guide students and to motivate their creativity and entrepreneurial spirit. Turning now to the research function of the university, we should note that, following Romer [31], human capital is an object-like kind of good, for it misses the non-rival aspect that characterizes “software.” Software creation is the result of research. Most often, the software produced at universities is non-excludable, being published in scientific journals freely accessible to the scientific community and to the society at large. The public good characteristic of non-excludable software creates the issue of individual appropriability. Since there are no private incentives for the production of this kind of knowledge, a market structure does not yield the motivation for scientists to produce. One way out of this problem is to make this software excludable using intellectual property protection, namely through patents. Patents are a way to allow the providers of innovations to benefit from a monopolistic position that yields them the benefits from the use of their ideas. However, the production of excludable non-rival software is not specific to universities. In fact, it is being done increasingly at the firm level. The real different type of product that comes out of university research is the public knowledge associated with basic and applied research. But the issue is, then, what are the incentives to research at the university and how to take advantage from its output. The answer lies in the rules of engagement of the scientific community, the result of a long and complex process of evolution. Stephan [32], following the seminal sociological work of Robert K. Merton, described the functioning of the scientific community as being based on a “winner-takesall contest” set of rules. This means that creativity is prized the most: the first scientist to achieve a result gets all the credit, and all similar ensuing results from other scientists are ignored. This type of work ethic has led to a type of appropriability that yields the necessary private benefits for production. In summary, the university is regarded as producing two outputs: (1) human capital, traditionally regarded as private good; (2) non-rival software, of which the non-excludable is most unique to the university.

4. The University’s Activities in the Knowledge-Based Societies As discussed in Section 2, the fundamental ability of human beings lying at the heart of economic prosperity is learning. We analyzed how economic theory has tried to conceptualize the mechanisms by which learning occurs. Very briefly, let us recall that learning can occur by doing, through education, and through research. As we reported in Section 3, the university’s activities related with the institutional features that accelerate learning are teaching and research. This section analyzes these activities in the context of the knowledge-based societies, in which the key contribution of the university should lie in maximizing the learning ability in a society. We discuss, in

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turn, teaching and research, and analyze the impact of public policies to maximize the universities’ impact. 4.1. HUMAN CAPITAL PRODUCTION—TEACHING

Human capital is traditionally regarded as a private good. Therefore, the argument that leads to the statement that students should pay the costs of their university education runs like this. Human capital is a rival good because it lies within the brain of each individual. Since there is no slavery, the linkage of human capital to every individual makes it necessarily excludable, and to be used at will only by the individual that possesses it. Being a private good, a market structure provides the necessary and sufficient conditions for a Pareto efficient “production” of university level human capital. Students are willing to invest in their education in order to accumulate human capital. The expectation is that they will possess a differentiating factor in the labor force for which employers are willing to pay a premium, as we discussed in Section 3. However, there are well recognized huge externalities in having a large proportion of the population educated and, particularly, with a university level of education. This, with other social and political arguments, leads to a public support for education in virtually all countries with the possible exception of Japan. The problem is that, in a time of tight public budgets, the argument that human capital is a private good can subdue the argument in favor of externalities, leading governments to ask for students to pay increasingly higher proportions of their university education. In the knowledge-based economies this can be particularly dangerous because the question we have to ask is: what is the minimum level of knowledge that is required for the labor force? The criterion must be related with a maximization of the learning ability of the individuals. In the Industrial Revolution, for example, the education level required for everybody to learn the emerging techniques of production at the time was what we now call primary education. Everybody should know, at least, how to read and count, in order to face the demands of a new system of production. This effectively made basic education a public good, and the differentiation in the education level grew to the high school and university tiers. In the knowledge-based society, the minimum level of training, we contend, is one that maximizes an individual’s ability to learn. There is, therefore, a need for a training in learning. University education may soon be the minimum level required to yield the population this skill. And the question we have to ask then is: is not university education increasingly a public good, very much like basic education became at the turn of the century? It may be indicative that in all the recent elections in the major developed countries the increase in the level of education was a key, if not the main, political point. President Clinton wants to make “university education as universal by the year 2000 as high school education is today,” and Tony Blair selected education as his government focal issue for the United Kingdom. This perspective forces us to reflect on the need for an increase in the public funding for universities, even if the issue is only teaching. Clearly, there is also a need for a contribution of the students, but the logic of emphasizing the private nature of human capital and the tendency to generalize market structures in search of efficiency, may hinder the more fundamental and deeper issues that we raised. At least, it seems, there is a need for some caution to counter this tendency. 4.2. SOFTWARE PRODUCTION—RESEARCH

University research, at least in terms of the common way to define R&D results in non-rival knowledge, that we will refer to as software. As we discussed in Section

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212 TABLE 2 Shares of R&D Expenditure Countries (year) Japan (1995) USA (1995) Germany (1995) France (1994) England (1994) Belgium (1995) Denmark (1995) Greece (1995) Spain (1995) Holland (1995) Ireland (1995) Italy (1995) Portugal (1995) EU average (1994) Korea (1994)

Universities (%)

Firms (%)

20.7 15.7 18.9 15.9 17.5 26.2 22.8 40.7 28.9 24.9 21.0 20.9 33.8 20.0 8.2

65.2 71.0 66.1 61.6 65.2 66.5 58.3 26.8 51.1 53.0 67.8 57.0 19.8 62.1 73.1

Source: OECD [3].

3, there are two broad strategies that yield private incentives for software production. The first strategy relies on giving the scientists economic rights over his or her discovery, through the enforcement of intellectual property. The scientist may then release the results at his or her will, and ask for a monetary payoff for external use of these results by others. The second strategy relies on the rules of engagement of the open science community. Results are made freely available to all, and the reward is based on the priority of discovery, which yields academic reputation and is reflected in promotions and income. In summary, the first strategy is one in which research results are privatized, and the second one in which they are public. The privatization of research results has been the primary route of companies that engage in R&D. Naturally, companies are interested in receiving the payoffs of their efforts by achieving a monopolistic position through the use of, for example, a patent. The production of new knowledge by companies has increased dramatically in the knowledge-based societies. In part, this results from the dynamics of the global competition, in which innovation and creativity are increasingly prized over more traditional competitive advantages such as costs. Table 2 illustrates the share of national expenditure of R&D made by universities and by firms, and shows that the first accounts usually for about 10%–20% of the total expenditure in developed economies. Although Table 2 data is a measure of input, and not output of knowledge, it clearly illustrates the commitment of firms to producing knowledge. And since this knowledge is privatized, a market structure yields the incentives for production. However, it is widely acknowledged that the public university research results are necessary for the long-term prospects of economic progress, and increasingly so in a knowledge-based economy. Now, under financial stress, a likely route for universities is to make the software they produce excludable, by issuing restrictive intellectual property rights, namely through patents. The rationale for this would be to capitalize on the economic relevance of the results, getting the private benefits at the expense of making them freely available to the society. This “privatization” of R&D results may have extremely negative effects not only socially, as the public good characteristic of

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this software is lost, but even for the universities themselves, as the university R&D results are often distant from practical commercial applications [33]. In terms of the innovation model proposed by Kline and Rosenberg [34], this would mean that the links between R&D and the available knowledge pool would be severed. Consequently, firms would have to muddle within a pool of existing knowledge less vibrant, dynamic, and diversified than would occur had the universities kept the public good characteristics of their research. Therefore, the public funding of university R&D is key to keeping, and possibly increasing, the levels of public research results that are available socially. Moreover, there are huge externalities in terms of learning abilities for those that perform R&D at the universities. Engagement in R&D at the graduate, and even at the undergraduate, levels leads not only to the production of knowledge, but also to a training in routines of search, inquiry, initiative, risk taking, and entrepreneurship. In summary, performing R&D enables the individual’s ability to learn, so that increasingly linking university research to teaching should be an important target for the university in the age of the knowledge-based societies.

5. Conclusion: The Preservation of the Institutional Integrity of the University Our main thesis is that, in the current context, restricting public funding to universities may hinder their institutional integrity. It is not change that endangers the university, but rather a transfiguration of an institution that has proved well over the centuries into a hybrid that yields unknown and potentially harmful outcomes. This can put at risk our common future in the knowledge-based societies of the present, but especially, of the future. More specifically, we argue for a sustained and increasing public funding of the university activities, both teaching and research. This kind of public policy will avoid the tendency of universities to devote more efforts to a commercial perspective on the execution of teaching and research, thereby hindering the fundamental public aspects of university education and R&D results. These are unique to the universities in our current institutional setting, and their preservation will be ever more important as the evolution to knowledge-based societies takes hold. A key issue in promoting the public funding of university research is the need for its increasing link to teaching, as a strategic policy to maximize the learning ability in a knowledge based society. References 1. Carac¸a, J., Conceic¸a˜o, P., and Heitor, M. V.: On the Definition of a Public Policy towards a Research University, Higher Education Policy (in press). 2. Brooks, H.: Research Universities and the Social Contract for Science, in Empowering Technology. L. M. Branscoomb, ed., MIT Press, Cambridge, MA, 1993. 3. OECD: Universities Under Sucrutiny. OECD, Paris, 1987. 4. Rosenberg, N., and Nelson, R. R.: The Roles of Universities in the Advance of Industrial Technology, in Engines of Innovation. R. S. Rosenbloom and W. J. Spencer, eds., Harvard Business School Press, Cambridge, MA, 1996. 5. Eicher, J.-C., and Chevalier, T.: Rethinking the Finance of Post-Compulsory Education, International Journal of Educational Research, 19, 445–519 (1993). 6. Weiss, C., and Passman, S.: Systems of Organization and Allocation of National Resources for Scientific Research, Knowledge: Creation, Diffusion, Utilization 13(2), 102–149 (1991). 7. Conceic¸a˜o, P., Gibson, D., Heitor, M. V., and Shariq, S.: Towards a Research Agenda for Knowledge Policies and Management. Journal of Knowledge Management 1(2), 129–141 (1997). 8. Alic, J. A.: Technological Change, Employment and Sustainability, Technological Forecasting and Social Change 55, 1–13 (1997). 9. Salomon, J. J.: The Uncertain Guest: Mobilizing Science and Technology for Development, Science and Public Policy 22(1), 9–18 (1995).

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Received 3 September 1997; accepted 6 Feburary 1998