Diversity and integration of science and technology policies

Technological Forecasting & Social Change 74 (2007) 1 – 17

Introduction

Diversity and integration of science and technology policies Pedro Conceic¸a˜o *, Manuel V. Heitor Center for Innovation, Technology and Policy Research, IN+, Instituto Superior Te´cnico, Lisbon, Portugal Received 25 April 2006

Abstract This paper discusses recent conceptual approaches to technical change, based on an emerging diversity of policies and increasing binstitutional specializationQ and clarification of the role of the private and public incentives to support S&T. This fact is reflected in the trend in developed economies towards increasing private investment in science and technology and we argue for the need to promote public policies in modern societies fostering competence building. This broad concept has motivated the work behind the present special issue, which was launched during the 6th International Conference on Technology Policy and Innovation (ICTPI), hold in Monterey, Mexico, in the summer of 2003. Under the broad designation of bConnecting People, Ideas, and Resources across CommunitiesQ the Conference brought together a range of experts to discuss technology, policy and management in a context much influenced by a dynamic of change and a necessary balance between the creation and diffusion of knowledge. Thus, this special issue includes a set of extended and revised contributions to the Monterey conference that are largely grounded on empirical experiences of different regional and national contexts. The aim of this introductory paper is to set the stage for these contributions, with an original contribution on possible views for emerging science and technology policies. D 2006 Elsevier Inc. All rights reserved.

1. Introduction: relying on science and technology policy? Why the focus on science and technology policy? As we emphasized in earlier papers, learning can occur in many shapes and forms, some of which are informal and other are formal [1]. The institutions * Corresponding author. E-mail addresses: [email protected] (P. Conceic¸a˜o)8 [email protected] (M.V. Heitor). 0040-1625/$ - see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2006.05.001

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and organizations that comprise the national and regional systems of science and technology attempt largely to formalize and accelerate the learning process for individuals, firms, and nations. Thus, by looking at this particular set of organizations and their networks, and institutions, we could be able to suggest routes for policy that can positively influence the conditions for inclusive development through learning. The challenges for policy in order to move towards inclusive development and wealth generation are really twofold. First, what can be done at the regional and national level to start and sustain learning networks and trajectories that can lead to wealth creation? Second, how can the overall global learning processes be made more inclusive, so that fewer countries are excluded, extending the reach of the learning networks globally? At the national level, it is increasingly clear that innovation is not a direct consequence of R&D. In the academic literature, the lack of validity of the linear model of innovation has been repeated ad nausea, but the fact remains that it still informs much of the policy rationale for investing in R&D. There is no question that the ideas that result from formalized knowledge exploration activities lead, in the long-run, to innovations, but to expect this to be so in the short run is misguided both for firms and governments. Kortum and Lerner [2], for example, show that venture capital is probably much more effective in promoting innovation than R&D at the firm level. This does not mean that firms and governments should stop doing R&D, but rather that they should do it for the right reasons. And there are many, from promoting human capital, to extending the frontier of knowledge. But in terms of public policy, the realization that innovation and R&D are not as connected as once thought is particularly important. This realization means the firms may lack even more incentives to perform their own R&D as previously thought, and thus require a stronger intervention of the public sector. This may be particularly important for late industrializing countries, with scientific and technological systems not yet fully developed and matured. Often these countries show very low levels of private commitments to R&D, with disproportionate high government expenditures in R&D. In a previous paper we have shown that the structure and financing of science and technology activities has undergone a change characterized by a shift from relying and supporting public science to a stronger emphasis on bmarket-basedQ incentives for science, technology and innovation [3]. Given the strong economic performance of the United States over the 1990s, this shift has influenced policies in most OECD countries, and especially in Europe. We noted that, from an analytical perspective, the continuation of this shift all the way to a point in which there are only private incentives is not desirable. In fact, for many authors, the trend as it exists presently is already reason for concern, since rather than what theory prescribes–that there should be a mix of public and private incentives to science, technology and innovation–we may have reached a situation where incentives in the United States are too biased towards the private side of the mix. In fact, the United States has historically pursued a wide range of approaches to encourage research and to build research infrastructure. New approaches have been adopted over time as the nature of the research/innovation endeavor evolved. The infrastructure is today quite diverse and robust with multiple performers. Similarly, the set of incentives to encourage research is diverse. Given the high uncertainty surrounding scientific research and innovation, this robust research infrastructure system minimizes the risk of poor targeting of research priorities, and the mix of public and private incentives strengthens this robustness. It is clear, in fact, that along with private incentives, public policy is needed to mobilize investment of social resources in new technologies and to insure the health of the overall enterprise.

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It is a brushQ to understand, and to copy, the U.S. system as being too driven by private incentives for S&T. bBlanketQ recommendations to enhance property rights or to limit public resource allocation, based on the U.S. experience, may be misguided. Even if there is a clear shift towards more private incentives in the United States, there is a long history of past investments and a current division of labour or specialization that cannot be replicated in systems with a lower scale and complexity. The key message from the U.S. history is that of a diversity of policies and increasing binstitutional specializationQ and clarification of the role of the private and public incentives to support S&T. With the hindsight gained from the discussion above, we can also bexplainQ the increased need of public intervention for science and technology policies, as resulting from the non-rival character of software. In this paper we argue that it is crucial not only to make available financial resources (namely public resources), but also to do so in a way that provides the right incentives for S&T organizations to hook up in learning networks that can generate localized social capital and endogenous growth dynamics. That way is definitely not unique and depends on local conditions, roots and trajectories, which raise the question of inclusive development [4]. We will start by analysing human resources developments over the last century that are associated with technical change and, then, we will continue by discussing the pattern of innovation studies and policies that have emerged during the last decades. The analysis is oriented towards the development of engineering world-wide and of its increasing complexity, as reflected on the increasing non-linearity of innovation, but it considers complementary and broader approaches that have called our attention for the need to emphasize the development of human resources and organizational and institutional capabilities [5].

2. Building technical competences for knowledge accumulation It is clear from the literature that the welfare of individuals, organizations and countries is based, particularly since the 1st industrial revolution, on the creation, diffusion, and use of technology. During the last decades of the 20th century, this reality was reflected in the trend in most developed economies to promote investments in high technology, research and development, and in technical education, as well as investments in education and culture. In this context, Fig. 1 compares the share of university degrees in various OECD countries by the end of the 20th century, illustrating the relative weakness of late industrializing countries in terms of technical qualifications (considering both engineering and natural sciences). Fig. 2 compares the total number of students in the higher education system in a group of small European countries, while Fig. 3 shows the percentage of engineering graduates in the active population aged between 25 and 64 in several OECD countries throughout the 20th century. These indicators are clearly limited in scope, but do represent the international promotion of the scientific and technological base as a principle to guide developed countries. According to Romer [7], the role of public policies for science and technology training is particularly critical for the economic growth over the long-run. These policies have accounted for the fast increase in the number of engineers and scientists in the United States from the postwar period to the 1970s. In fact, the study of the relationships between knowledge creation and economic development is an increasingly important component of research on public policies that seek to stimulate growth. It is legitimate to argue about changes to the traditional way of thinking about the economic growth, and to question the role

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Fig. 1. National share of university degrees among different fields (1997). Adapted from [6].

played in that process by the technical education and science and technology systems. In short, these reflections are based on a new conceptual approach to economic development, in that the knowledge accumulation appears as a fundamental engine of the development process. For many authors, the idea of knowledge-based economies is still a concept and more than a reality that can be objectively characterized. This characterization has been based essentially on stylized facts, like the growing incorporation of knowledge in physical products, the increase in the value associated with ideas vis-a`-vis material goods, and the strong importance of services. Traditionally, economic growth has been explained as resulting from the growth of labour and capital factors, and of technological change. It is important, however, to rethink the way these factors occur in the process of economic development. Regarding the contribution of labour, the evidence is that the quantitative increase in the population is insufficient to account for the economic growth verified. This is because the developed economies increasingly produce intangible factors, creating employment, mainly in the service sector, where education and professional qualifications are requested. Thus, for the growth and employment creation, it is crucial to increase human capital so that the access to more and better skills, namely through education, can be promoted. As far as the contribution of capital is concerned, the accumulation of intangible assets has gained importance vis-a`-vis physical capital. Thus, the importance of knowledge does not only appear through the contribution of technological change, which has led to the need to rethink the traditional patterns of

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Fig. 2. Total number of students in the higher education system for the period 1960–2001 in a selected group of small European countries. Adapted from [1].

explaining economic growth. For instance, the new growth theories include many of these efforts, suggesting that knowledge accumulation can be understood as learning and is the most important factor to explain the process of economic development at long run.

Fig. 3. Percentage of graduated engineers in active population aged between 25 and 64 years old in a selected group of OECD countries. Adapted from [1].

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What does this discussion have to do with technical change and the role of technical education and of research in the economy? To introduce this question, it is important to consider, in the first place, the traditional perspective of the economic growth, for which growth results from the accumulation of the work and capital factors, as well as of the technological change. The introduction of the technology factor, that is due to Solow [8,9], proved to be essential for the empirical explanation of the measured growth levels. The simple accumulation of the work and capital factors has never been enough. But technology always appeared as being external to the economic process, an exogenous component, as mentioned in the specialized literature. According to the new theories of the economic growth the perspective is completely different. The accumulation of capital, or, in the new nomenclature, of objects, continues to be essential. But the only source of continued growth production is knowledge: new ideas to produce new objects and to organize the existing objects in an increasingly efficient way, on one hand. On the other hand, new and improved skills allow the implementation of ideas and the use of objects. For example, Romer [10] has a simple principle: new ideas and new and better skills, that is, more knowledge, are really responsible for productivity and efficiency gains, resulting in economic growth. As a matter of fact, mankind has been constrained since the beginning of civilization by the natural resources and energy with which our planet was gifted. Human development only results from the knowledge that is generated and accumulated, allowing the rearrangement of these resources in an increasingly productive manner. Thus, in the new growth theories, knowledge, in first place, is not limited to technology, and, in second place, it is not exogenous. It corresponds, alternatively, to new ideas and skills, not only in the technological field, but also in the social, legal, political, and administrative fields, among others. If it is true that knowledge accumulation accounts for the largest contribution to growth, it is now important to briefly discuss how that accumulation happens. In other words, knowledge accumulation means to learn, not in the strict sense of an individual learning, but in a wider context, where one can speak about organizational, national and regional learning. The formalization of the economic development process in the new growth theories follows the model originally proposed by Arrow [11]. It is important to briefly focus on the analysis of Arrow since it contains the essence of the foundation of economic development as a learning process. Instead of following the orthodoxy at the time, which attributed the inexplicable growth component according to the accumulation of the work and capital factors to technological change, Arrow argued that the experience in the capital use led to an increase in knowledge used in the production. In a more prosaic way, Arrow formalized in a relatively simple model the idea that the workers of a company learn with the use of production means, increasing the company’s productivity. Therefore, learning, i.e., knowledge accumulation, appears as the engine of efficiency increases, resulting in the economic growth. It is interesting to notice that Arrow chose an informal way of learning, learning by doing, to support his thinking. In this model, knowledge is entirely accumulated under the form of skills. The contribution of the new theories of the economic growth was, precisely, to extend this thinking to other learning types, as well as the accumulation of ideas, starting from the moment that Romer [12] demonstrated the generality of the Arrow’s arguments. Two other models of new theories have been pointing out the formal institutional mechanisms that exist in our society to accelerate the learning process, namely education and research. The models that depend on education follow Lucas’ seminal work [13], while Romer [14] and Grossman and Helpman [15] are the canonical references for the models that have, as an endogenous growth source, research and innovation.

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Pasinetti [16] articulated in a very clear way the idea that learning is the source of growth, having observed that: bSince man is able to learn, advancement occurs because the next society has always a better departure point than the previousQ. In this context, Pasinetti considers that the human capacity to learn is the key-factor for economic growth, a perspective that is the foundation for the new growth theories. Nevertheless, according to Pasinetti, the sources and growth mechanisms are different from those considered by the new theorists of economic growth. Analysis shows that some of the more considered authors, who are linked to the new economic growth theories, analysed knowledge accumulation under the form of skills, education being a formal learning process. Additionally, there were also developed models where the accumulation of ideas stems from the effort in research, another formal process of learning. In this context, two aspects should be pointed out. Firstly, in the early 1990s the focus was on the study of the accumulation of ideas through R&D, a tendency that has been reinforced in recent works. There are, at least, two reasons for this. On one hand, the study of the informal learning process is more complex and less likely of being tested empirically. This leaves room for reflection about the accumulation of ideas through R&D, since studies on the education role have a considerable past, bearing in mind the theories of the human capital in the 1960s. On the other hand, the truly remarkable modern day fact is the growing codification of knowledge, and the potential that the digital economy bor the information societyQ reserves for us. Secondly, there has been a recent interest in analysing the economic implications of the resulting learning processes of social interaction, mainly under the scope of the binformation society.Q Actually, this aspect introduces a new vision for the education system, namely regarding the radical change of the magisterial teaching towards an announced learning, which is directly associated with continuous training (lifelong), and to the need to manage different demands and a multifaceted public. Additionally, the fact that informal processes of learning are being shared by a diversified spectrum of institutions makes it possible to open new perspectives to the science and technology system and to create and disseminate knowledge.

3. Learning from innovation studies and policies At the onset of the 20th century few could have guessed the importance that the then nascent technologies would have in the improvement of the quality of life over the ensuing century. As we enter the 21st century the promise of further improvements based on new and deeper applications of technology, and engineering systems in general, is a reason for optimism. While it is difficult to forecast the exact shape and form that new technologies will assume, it is safe to say that there are a number of challenges to which technical competences can provide, at least partially, a response. The vision supporting this book is based on an identification of some of those challenges that, although related to technology, must be understood in a context in which the integration of a diversified social context and economic fabric in an increasingly open and interconnected world cannot be ignored. The assumption is that several now disjointed disciplines must join efforts to provide new solutions to mobilize people, ideas, and tools to help to catalyse the strong progress in information and communication technologies needed to secure the necessary creativity for a sustainable future worldwide. This chapter discusses these challenges in terms of what we know today from successive

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science, technology and innovation studies, partly devoted to improve our understanding of ways to accelerated forms of breliable knowledgeQ [17]. 1.1. Understanding technological innovation: from the Manhattan Project to the systemic understanding of innovation Under a context of not only increasing complexity, but also uncertainty, it is well known that the systematic coordination of S&T at an international level, and the consequent development of science, technology and innovation policies are a result of the Second World War and, in particular, of the Manhattan Project, that has materialized the use of nuclear physics [18–20]. Science was removed from university and public laboratories and exposed to society, primarily to develop military technology for immediate application. The creation of the National Science Foundation, in 1950 in the United States of America following Vanevar Bush’s influential report launched immediately after the end of the war marked the role of public funding to university R&D [21]. In fact, Bush noted that: Historical development has given the sanction of tradition to the prominent role played by universities in the progress of pure science. [. . .] Several factors combine to emphasize the appropriateness of universities for research. With the bcold warQ and the brace for the spaceQ, this process increased substantially, with the national budgets for the science and technology reaching very significant figures, especially in the United States, with public-funded R&D programs for different technologies [22]. As Salomon [23] referred, after the Second World War, first for strategic reasons, and then, on behalf of the economic growth and competitiveness, science became, irreversibly, a subject of the State. If the 1950s and 1960s were characterized by a widespread growth in economical terms, that in fact has allowed financing the expansion of education and S&T systems, the 1970s, on the other hand, witnessed an effective attenuation of the economic growth level and, in particular, a decrease in productivity growth often designated by productivity slowdown [24]. This attenuation had, together with other factors, deep implications on the development of education and S&T systems, namely through studies that demonstrated no direct correlation between the resources allocated to R&D activities and economic results. Nevertheless, the 1970s attested fast scientific and technological transformations, resulting in new and important technologies that could have even have improved the economic performance, accompanying the regeneration of obsolete technologies. The perplexity that resulted from this apparent contradiction was named productivity slowdown paradox [25]. An important consequence of the effort to justify this paradox was a deep alteration in the perception of the relationships between science and technology and the economy. In fact, until the early 1970s, the dominant understanding was that technology was generated in a system external to the economy, which made inventions that would come into the economic system at a later date, corresponding to an innovation. The explanatory mechanisms of those processes were linear, of the pipeline type, leading to technology-push models (in that a new technology provides commercial explorations) and market-pull (in that the perception of market needs drives the R&D effort). These were called linear models of innovation. Thus, during the 1970s the conscience that it was necessary to rethink the role of S&T in a way beyond considering technology a closed box (bblack boxQ, in the terminology of Rosenberg) has emerged. The political implications of these perceptions led to the favourable management of S&T systems, considering that it was necessary to decide on scientific and technological investments adapted

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to the resolution of specific problems. As a result, developed countries, namely at the OECD level, have integrated S&T policies in the remaining economic policies, clearly seeking to innovate and promote economic development. In the 1980s, the reflection on the relationship between technology and economic and social development has new details. The new theories of economic growth and the descriptions of the dynamics of technological change and innovation not only have broadened new horizons, but also uncertainties, in the way science and technology can be effectively managed and implemented. Within a broad set of different approaches, the OECD program designed to explain the relationships between the economy and technology considers technology endogenous to the economy, as it is generated and disseminated through a complex fabric of relationships and interactions between companies, universities and laboratories, resulting in innovation [26]. Thus, the linear models of the 1960s gave place to the understanding of innovation as a complex, non-structured process involving institutions of the education and the S&T systems and companies, under which R&D activities determine, and are determined by the market, in a way that was first acknowledged and modelled by Kline and Rosenberg [27] through the so-called binteractive model of innovationQ (or bchain-linked model of innovationQ). From this point, innovation is depicted as a multi-layer process with multiple feedbacks between different activities and functional units of the firm. Innovation does not flow linearly from R&D and does not result only from knowledge generated within the firm. The 1990s reinforce this notion, but requiring a more comprehensive understanding of other external effects associated with the processes of worldwide economic integration (i.e., bglobalizationQ) and of the competitiveness imperative. During the 1980s and 1990s, the bEuropean schoolQ equally developed the more sophisticated approaches to innovation with the approaches led by Rosenberg and others across the Atlantic. Freeman and Soete [28] summarize the main conclusion of this school, and Dosi [29] provides an integrative review of the main differences between these perspectives and traditional neoclassical approaches in economics. The fundamental difference, at the microeconomic level, is associated with the rejection of the representative production function. Nelson and Winter [30] attempted to provide an epistemological alternative to microeconomic foundations of neoclassical modelling. Mytelka and Smith [31] consider the co-evolving process of policy making and theory building, and note the way the linear perspective still informs much of today’s public perceptions about innovation, as well as policy design and implementation. The reliance on simple and direct indicators such as expenditure of R&D by the private sector reflects the dominance of the linear perspective. We do not question the importance of these and other indicators, we are merely asserting that they provide an incomplete description of the innovation process and are tied to the linear perspective. 1.2. Extending the conceptual understanding of innovation During the 1990s several attempts have been established to improve our understanding of the increased complexity associated with innovation and technical change. Romer [32] recognizes the importance of what he calls appreciative theories of growth and innovation (following the introduction of the term by Nelson and Winter) in helping more formal approaches to better describe the richness of the innovation process. Somehow, the link has been hard to accomplish, possibly due to insurmountable epistemological differences between scholars in the neoclassical tradition and others of more appreciative nature. In fact, Romer [14] constructs his theory of endogenous growth drawing on the

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non-rival nature of ideas. Dasgupta and David [33] advance new ideas about the economics of science building also on the same principles associated with the special characteristics of knowledge. Using the economic characteristics of knowledge is useful not only as a modelling tool, allowing the development of new conceptual approaches, but also as a guide for policy [34]. And, in a series of papers, we have explored the implications of the non-rival character of ideas and the rival character of tacit knowledge to advance policies associated with higher education policy [35,36]. Beyond the studies about the economics of knowledge, an important part of recent research on innovation has concentrated on institutional and organizational issues. In fact, enterprise organization has become central to the policy debate on the sources of European competitiveness, leading to the promotion of many forms of organizational change. As noted by Lorenz [37], this has derived from the growing recognition that ICT investments alone generally fail to deliver improved enterprise performance, requiring adequate human practices, among a complex set of other interrelations. As a result, the need for organizational innovation has promoted in recent years a large amount of works contesting a traditional, neo-classical understanding of the firm and the economy [38]. The emerging knowledge perspective is concerned with the role of technological change and firm behaviour in economic growth. The foundations of this approach can be found in the work of Schumpeter [39], but its main development and application were done by Nelson and Winter [30]. In their approach, the firm is understood essentially as a repository of knowledge, which is translated into routines that guide organizational action. Building on these perspectives and on earlier work in organizational theory that emphasized the mechanisms for the growth of firms, a knowledge based vision of the firm has been under development in the last decade, offering new insights for strategy and management theory [40]. It includes natural systems and institutional theory while embracing global perspectives. Also, it encourages multidisciplinary perspectives to better explore the meaning of competitive advantage in developing, acquiring, and using knowledge for enhanced products and processes and in better understanding the interaction between organizations and the economy in which they are embedded. In terms of the discussion above, it is interesting to note that we can identify at least three different ways of delimiting the systemic view of innovation [41], namely: (i) the innovation system as rooted in R&D; (ii) the innovation system as rooted in the production system; and (iii) the innovation system as rooted in production and in human resources. Lundvall [42] uses this latter and broader approach to analyse the interaction between technical change, organizational change and competence building in the case of Denmark and successfully concludes that the general economic climate in terms of the transformation pressure and the intensity of competition has a major impact on what firms do by way of technical innovation and organizational change. His analysis strongly supports the views expressed by Andreasen et al. [43] in terms of the need to focus European policy on stimulating organizational change in European firms. However, it is known that the Danish type of innovation system is intense in its use of bnational social capitalQ and before it can be considered as a benchmark, one must account for the question of social cohesion and its complex relationships with competence building and innovative capacity. 1.3. The emergence of studies on innovation, competence building and economic equality Following the analysis in the previous paragraphs, it is clear that a topic of increased awareness for innovation researchers has been derived from the need to better understand the link between competence (skills, education), and innovation (technological change). Carneiro [44] refers that bInstead of requiring

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a skill, . . . employers are seeking competence, a mix, specific to each individual, of skill in the strict sense of the term, acquired through technical and vocational training, of social behaviour, of an aptitude for teamwork, and of initiative and a readiness to take risksQ. Many instances can be given about the importance of building competence. Carneiro chooses a few, from the resurgence of the bhuman capitalQ literature–which has percolated to the language of everyday life–to the very idea of the knowledge-based economy. Carneiro also explores the implications of the importance of competence building to the individual and to the dynamics of innovation and presents the idea that it is important to nurture vocational identities. Vocational identities include, but are more than just, the individual knowledge base and the portfolio of competencies. These include attitudes revealing a preference for learning, in which bcompetence buildingQ considers also aspects such as the strengthening of identity and of a foundation of emotional stability and of self-esteem. Thus, the idea of competence building is, in this context, viewed in a much more comprehensive and deeper way, encompassing the individual in several dimensions. The link with innovation is made through the distinction between adaptive and generative learning, which are connected with the Schumpeterian cycle of creative destruction. In this context it has become a bcommonplaceQ to assume that technological change is (or has become) skill-biased, in the sense that it requires people with high skills [45]. Specifically, digital computers and, more generically, information technologies, are considered the btrend breaking technologyQ that is responsible for the inequality increases [46]. Alternatives to the skill-biased technological change include the perspective advanced by Bresnahan [47], who rejects the complementarity between computers and the human capital (or skills) of individual computer users. Instead, Bresnahan proposes an organizational complementarity between information technologies and telecommunications (ICTs) and highly skilled workers. In Bresnahan’s model, ICTs, instead of improving the productivity of individual workers, change the organizational structure of firms, reducing the needs for back-office workers, and increasing the demand for front-office workers and managers. Thus, skill-biased technological change has been advanced as an explanation for rising levels of income inequality. This explanation is grounded on the assumption that wages are the result of market clearing via the competitive pricing of the capabilities of people. Different capabilities are associated with different levels of skill, education, and seniority in the work force. High skill/education/experience is associated with a higher marginal product of labour and commands higher wages than low skill/ education/experience. The evolution of the difference between the prices of skill, the relevant issue in studying changes in inequality, depends on the interaction between shifts in the relative demand for more skilled labour over less skilled workers and changes the relative supply of skilled labour. There is, however, a second class of explanations, not necessarily excluding the labour market perspective outlined above, that puts much more emphasis on the role of institutions. Specifically, changes in the institutions that constrain the definition of wages override competitive forces in the dynamic evolution of income associated with rising inequality. These institutional changes include the weakening of unions–which erode the bargaining power of low paid workers–changes in pay norms (more contingent employment and pay), and the decline in the real value of the minimum wage, which, can constitute an important redistributive tool [48]. A standard division of OECD countries according to these two classes of explanations places the United States, and also the United Kingdom in the 1980s, in the realm of the labour market category, and the remaining OECD countries in the wage-setting institutions class [49]. The reason, it is argued, is that the labour market in the United States and the United Kingdom after the 1980s, is much more free from

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the strength of collective, centralized bargaining than the remaining OECD countries. However, even for the OECD countries other than the United States and the United Kingdom, the skill supply and demand hypothesis has been gaining momentum and thus we will spend some more time analysing this hypothesis. There are two dimensions to the skill supply and demand story. The first concerns the validity of the assumption that labour market mechanisms are dominant in driving the dynamics of inequality. Assuming that the labour market provides a good framework for analysis, the second dimension is related with the ultimate causes that originate the labour market responses that generate inequality. At a very fundamental level, some scholars reject totally the existence of the validity of considering the existence of a labour market rewarding skill and human capital. Some of the critiques of the human capital theory and the returns to education occur almost at the epistemological level. In fact, many sociologists oppose the mainstream economics theory of human capital, arguing that cultural and institutional factors are much more important in determining wages. Some authors have also argued that sociological and psychological factors, rather than economic factors associated with supply and demand for qualified labour, are dominant in setting wages. However, if one is ready to accept the existence of a labour market where wages reward, at least partially, productivity and skill, Katz and Murphy [50] provide strong evidence that supply and demand go a long way in explaining the patterns in the evolution of inequality. Most of the recent studies on inequality that focus on a single-country longitudinal analysis of the evolution of the dispersion of income follow Katz and Murphy. Examples of the same methodology applied to other single country studies include Schmitt [51] for the United Kingdom, and Edin and Holmlund [52] for Sweden. Blau and Kahn [53] apply a similar procedure to a cross-section of OECD countries for a single year. This discussion not only clearly highlights the link between competence (skills, education) and innovation (technological change), but also shows the need to bring to our attention the relevance of social cohesion (economic inequality), as recently discussed in the volume edited by Conceic¸a˜o et al. [5]. Today it is well known that it is through the diffusion process that technological innovations are translated into wide economic impact, as more and more people and firms consume and use the new products or processes. And if we accept that this increasingly generalized usage of technological innovations fuels not only increases in well being, but also the conditions to generate further innovations, then one cannot escape the importance of demand conditions for economic and technological prosperity. In fact, historians of economic evolution have shown that demand conditions were crucial in the process of early industrialization in the United States. Rosenberg [54] describes the demand conditions that were conducive to the earliest stages of industrialization in the 19th century. In an economy that was primarily agricultural (in 1810, 80% of active Americans were in agriculture), the most important resource was arable land, which was plentiful. This was, indeed, the most important source of wealth for economically active Americans, and the availability of land ensured a fairly equal distribution of this resource. This, in turn, meant that food prices were relatively low, allowing, for the same level of income, a higher margin left to buy non-food products. This scenario is in stark contrast with the situation in Europe, where poor peasants and farm labourers had virtually no income beyond that needed for subsistence needs. The American conditions fueled also a rise in fertility that was translated into a large population growth that could go and occupy even more of the still available fertile land, in a virtuous cycle of development. Therefore, the low level of economic

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inequality, coupled with a relatively high level of income per capita, generated the conditions that allowed for a demand of mass-manufactured goods. Related anecdotal evidence is provided by Rosenberg making use of Henry Ford’s strategy in the early 20th century: bThus, out of the social and geographic conditions of land-abundant America emerged a set of tastes and preferences highly congenial to a technology capable of producing large quantities of standardized, low-priced goods. These circumstances even left their indelible imprint on the American automobile in the early years of the 20th century. The Ford Model T was designed in a manner which strongly resembled the horse and buggy, and the primary buyers were farmers for whom a cheap car offered a unique opportunity for overcoming rural isolationQ. Therefore, demand conditions were important determinants in the diffusion of new technologies. In fact, in Rosenberg’s argument, they were crucial to creating a new industrial system out of an agricultural society. An important component of the demand conditions was a relatively high level of income per capita and, equally crucial, a relatively egalitarian distribution of the marginal income available beyond the one needed for subsistence. Inspired by this analysis of the interaction between inequality and technology, we are interested in understanding whether, with the current wave of technological innovations, there is also a relationship between levels of inequality at the country level and the rates of diffusion of technology. The argument we are advancing here is that social cohesion, beyond the issues associated with ethical judgement and justice, can be of importance to efficiency as well. Galbraith [55] proposes an interpretation of the economic success of the United States over the 1990s that is associated, precisely, with this view. For Galbraith, the reasons for the success of the American economy can be associated with the creation of a more equitable society, in which access to education is more generalized and where income is more equitably distributed than in Europe. The comparison between the distribution of income across Europe and across the United States is based on taking Europe as a whole. That is, instead of comparing the United States with individual European countries, Galbraith et al. takes into account the large differences that exist across European countries [56]. In Galbraith’s view, the United States has made a large effort over time to create a more equitable country through the reinforcing of the role and services provided by the U.S. Federal government, as the state governments become less and less relevant, especially in the determination of macroeconomic policies. In particular, the role played by the U.S. Federal government in terms of social policy has been crucial in reducing geographic inequities. Additionally, an impetus should be given to the creation of large, publicly funded European universities in the less developed zones of Europe, mimicking the land grant act of the United States in the mid 19th century, which led to the creation of focal points of development, especially in terms of innovation, dispersed around the United States. The major threat to social cohesion in Europe is, according to Galbraith, the relatively high level of unemployment [5].

4. Introducing this special issue The analysis above shows that it is legitimate to question the traditional way of viewing the role of science and technology policy and to argue for the need to promote public investment in competence building, together with a diversified mix of mechanisms to foster science and technology. This broad

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concept has motivated this special issue, which integrates a set of new contributions addressing complementary aspects of relevance towards improved understanding of the process of technical change. Miller and Clarke, in the following paper, analyse the hidden value of air transportation infrastructure and argue that countries must invest in air transportation infrastructure to ensure their ability to meet current and future demand for aviation services. The authors use system dynamics to model different strategies for infrastructure delivery. These strategies are defined by three variables: the amount of capacity increase, the time to deliver the capacity and the congestion threshold that triggers the need for capacity delivery. Monte Carlo simulation is used to take into account multiple sources of uncertainty. The model shows that a strategy of capacity delivery based on small increments and short response times can yield more benefits than strategies that consider large capacity increases and long response times. Furthermore, in the specific airport example considered, it was found that a congestion threshold of 75% should be the trigger for capacity enlargements if strategies based on small capacity increments and 1 or 5 years to increase capacity are considered. The lesson for decision-makers is that congestion delays must be addressed with foresight. Michael Sable, in the third paper, discusses the impact of the biotechnology industry on local economic development in the Boston and San Diego metropolitan areas and shows that, although hightech industries such as biotechnology are coveted as drivers of economic development, the local development impact of these clusters of regional innovation is not entirely positive. This is especially true with regard to the impact upon the low and semi-skilled populations. In some regions, the new growth generated by high-tech clusters has converted relatively inexpensive open space into haphazard commercial and industrial use that has contributed to sprawl, transportation congestion, lack of affordable housing, and gentrification. Nuno Oliveira, in the fourth paper, presents another case study on obstacles to the growth of biotechnology. He take a closer look at the case of Portugal, a country where the industry has long been at an embryonic stage and argues that generalist, top-down measures to stimulate general technological development may not be appropriate to foster a sector composed of many unique characteristics. Evidence from several countries suggests that there is a group of specific factors which all have to be in place simultaneously to allow the emergence of a biotech industry. Thurik and Baptista, in the fifth paper, discuss the effect of entrepreneurship on unemployment and show that the industrial transformation from a managed to an entrepreneurial economy varies widely across Western countries. As an example, they provide some empirical evidence supporting the view that Portugal has been a relative outlier in regard to the effects of entrepreneurship on unemployment when compared with the OECD average. Although the nature of entrepreneurship may be different in the Portuguese case, due to a high proportion of bmicro-businessesQ created for subsistence which have little impact on growth and employment, this factor does not seem to be the primary reason for the observed discrepancies. The differences between observed levels of unemployment for Portugal and those predicted by a model based on OECD data seem to be mostly associated with macroeconomic fluctuations associated with European business cycles and EU bcohesionQ funding, as well as with adjustment costs to new technology adoption which lead to productivity slowdowns, thus increasing the time lag for the effect of entrepreneurship on employment beyond the OECD average. Maranto-Vargas and Gomez-Tagle, in the sixth paper, analyse the development of internal resources and capabilities as sources of differentiation of SME under increased global competition in Mexico. The authors found statistical evidence to suggest that business performance measured by growth rate,

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efficiency, productivity and shareholder’s financial returns, is positively related with the development of internal capabilities such as soft technology (methods and processes that support the firm) and hard technology (externally acquired equipment, in-house development of machinery and innovation in raw materials) and a strategy of continuous improvement, innovation and change. Results of this research support the propositions that the development of a geographical region or country should be an interaction between a number of constituents, namely government policies, the firms themselves and universities and research centers among others that align their strategies towards the development of geoeconomic regions. The final paper, by Moutinho and Heitor, considers the co-evolution of urban environments and information and communication technologies in terms of the social and cultural shaping of information technologies and related uncertainties for their application to regional and urban contexts. The analysis is based on observations in different digital cities and regions with the ultimate goal of increasing regional competitiveness, by promoting public awareness and participation in decision-making processes. It is argued that the territory is a basic infrastructure that justifies and invites for the construction of several layers of information, but above all for communication infrastructures and digital contents well arranged with local contexts. The analysis led the authors to suggest that while the role of public policies needs to be re-examined, the cultural and social shaping of information technologies requires the specific development of human-centered systems to support community building activities. This calls for the need to combine flexible infrastructures and adequate incentives with institutions, to foster the necessary context for digital cities to succeed.

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