The Economics of Knowledge, Innovation and Systemic Technology Policy

Francesco Crespi, Francesco Quatraro

• Publisher: Routledge
• Year: 2015
• ISBN:
  9781134468881
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

There is wide consensus on the importance of knowledge for economic growth and local development patterns. This book proposes a view of knowledge as a collective, systemic and evolutionary process that enables agents and social systems to overcome the challenges of the limits to growth. It brings together new conceptual and empirical contributions, analysing the relationship between demand and supply factors and the rate and direction of technological change. It also examines the different elements that compose innovation systems.

The Economics of Knowledge, Innovation and Systemic Technology Policy provides the background for the development of an integrated framework for the analysis of systemic policy instruments and their mutual interaction the socio-political and economic conditions of the surrounding environment. These aspects have long been neglected in innovation policy, as policymakers, academics and the business community, have mostly emphasized the benefits of supply side strategies. However, a better understanding of innovation policies grafted on a complexity-based approach calls for the appreciation of the mutual interactions between both supply and demand aspects, and it is likely to improve the actual design of policy measures.

This book will help readers to understand the foundations and working of demand-driven innovation policies by stressing the importance of compent and smart demand.

1 Knowledge, innovation and the different dimensions of systemic technology policy

Francesco Crespi and Francesco Quatraro

1 Introduction

Knowledge and innovation are increasingly recognized as being among the main ingredients to boost the competitiveness of countries and regions. In this perspective, the effectiveness of the policy instruments aimed at promoting the generation of new technologies plays a crucial role as a way to open up new growth paths.

The starting idea of this book is that the complex and distributed character of scientific and technological knowledge asks for an in-depth analysis of the different but complementary and strongly interrelated drivers of the generation of new ideas.

The importance of technological knowledge to economic processes has been recognized relatively late by scholars in economics. Nowadays, despite the variety of approaches to the issue, the consensus is basically unanimous on the boosting effects that the creation of new technological knowledge may have on economic performances.

The mechanisms by which knowledge can affect the performances of firms, regions and nations are manifold. New knowledge can be embodied in new and more effective machineries, or it can contribute to improve the design of the production process. This increases firms’ productivity. A new technology can be at the core of the development of a new product, which allows a firm to enter new markets or to improve its position in its usual market. This paves the way to new growth paths. Knowledge creation generates important spillovers at the local level, which are likely to benefit innovation dynamics of firms settled in a specific area. When new knowledge stems out of a radical discontinuity in the technological paradigm, spillovers are more likely to be exploited by new entrants than by incumbents, fostering the entrepreneurial process. These dynamics can also yield positive dynamics on employment and job creation in the long run, by stimulating self-enforcing growth processes.¹

Once the role of innovation in economic processes and the existence of a variety of mechanisms that lead to the generation of new knowledge are recognized, the relevance of setting a multi-dimensional system of policy instruments to activate and foster the different sources of innovation dynamics can be properly appreciated. Hence, as it will be clarified in the next sections of this chapter, the structure of the book is conceived as to show how the peculiar characteristics of knowledge call for an integrated system of policies to foster its generation and diffusion that takes into account all actors (including the public sector itself) and all forces involved in these processes along with their interactions.

2 Modes of knowledge production: from linearity to complexity

The increasing evidence about innovation dynamics has stimulated a wide body of literature dealing with the very mechanisms by which technological knowledge is generated, diffused and exploited. By looking at the evolution of the theoretical approaches to technological knowledge in the economics of innovation, one may identify three main turning points, which are also featured by coherent empirical approaches (Krafft and Quatraro, 2011; Quatraro, 2012).

The former explicit attempt to model the production of scientific knowledge dates back to the 1940s. Vannevar Bush’s report to the US president was dominated by a vision of knowledge accumulation as an outcome of a linear process like this one: science precedes technology development, which then comes to be adopted by firms, and finally affects production efficiency. This has long been the main reference text to students of science and technology, as Kline and Rosenberg’s critique to this representation came only in the 1980s (Bush, 1945; Kline and Rosenberg, 1986; Balconi et al., 2010). The empirical counterpart to such a theoretical approach can be found in Zvi Griliches’ 1979 paper, in which he proposed the famous extended production function. In this article, Griliches provides a formalization of the concept of knowledge capital stock, which is modelled by applying permanent inventory method to calculate the knowledge stock starting from R&D expenditures. It is clear that the application of lag generating functions to investments measures so as to get a stock implies an underlying sequential process that starts with R&D investments to yield a proxy of cumulated knowledge that in turn is supposed to show some effects on economic performances.

In the late 1980s and early 1990s, some scholars of science and technology started criticizing the linear model, by proposing an alternative view basically drawing upon systemic models of innovation based upon the interaction among different and yet complementary institutions involved in the complex business of knowledge production (Kline and Rosenberg, 1986; Gibbons et al., 1994). Moreover, the analysis of knowledge as a factor in an extended production function became an object of criticism. Actually, it was just a way for economists to preserve the basic microeconomic assumptions about production sets out of which firms take their profit-maximizing choice. However, such approach assumes the existence of a separate R&D sector that is partly responsible for the change in the production technology, and hence for the shift of the production function (Nelson, 1980). On the contrary, science and technology are far from being sharply differentiated and it is not possible to identify a one-to-one mapping from science to public institutions or from applied technology to private business firms. Different kinds of organizations take part in the process of knowledge production, like firms, research labs and universities (Nelson, 1982, 1986). This set of arguments has been on the whole well received in the literature dealing with knowledge production function (KPF), which has been articulated both at the firm and at the regional level.² Knowledge is no longer the mere result of cumulated R&D spending subject to decreasing returns. The knowledge production function provides a mapping from knowledge inputs to knowledge outputs, so as to accommodate the idea that knowledge is the result of the interaction of a number of complementary inputs provided by different research institutions.

The development of the knowledge production approach inevitably leaves a basic question as to what the micro-founded mechanisms are underlying knowledge production. In this respect, the interest in the cognitive mechanisms leading to production of new technological knowledge has recently emerged in the field of economics of innovation. This strand of analysis has moved from key concepts brought forward by Schumpeter (1912, 1942), who proposed to view innovation as the outcome of a recombination process. Most innovations brought about in the economic system stem from the combinations of existing elements in new and previously untried ways. Such innovations appear to be mainly incremental. Radical innovations stem instead from the combination of existing components with brand new ones.

The contributions by Weitzman (1996, 1998) represent the former, and very impressive, attempt to draw upon such assumptions. His recombinant growth approach provides a sophisticated analytical framework grafting a micro-founded theory of knowledge production within an endogenous growth model. The production of knowledge is seen as the outcome of an intentional effort aimed at reconfiguring existing knowledge within a genuine cumulative perspective. However, there is no particular focus on the constraints that the combination of different ideas may represent, especially when these ideas are technologically distant. The only limiting factor seems to be the bounded processing capacity of economic agents.

The recombinant knowledge approach is based on the following assumptions. The creation of new knowledge is represented as a search process across a set of alternative components that can be combined with one another. However, within this framework, a crucial role is played by the cognitive mechanisms underlying the search process aimed at exploring the knowledge space so as to identify the pieces that might possibly be combined together. The set of potentially combinable pieces turns out to be a subset of the whole knowledge space. Search is supposed to be local rather than global, while the degree of localness appears to be the outcome of cognitive, social and technological influences. The ability to engage in a search process within spaces that are distant from the original starting point is likely to generate breakthroughs stemming from the combination of brand new components (Nightingale, 1998; Fleming, 2001).

Recombination occurs only after agents have put much effort into searching within the knowledge space. This strand of literature posits that knowledge so obtained is complex, meaning that it comprises many elements that interact richly (Simon, 1966; Kauffman, 1993). This has paved the way to an increasing number of empirical works based on the NK model proposed by Kauffman (Kauffman and Levin, 1987), according to which the search process is conducted across a rugged landscape, where pieces of knowledge are located and which provides the context within which technologies interact.

Such a framework clearly has the merit to push the economic discussion about technological knowledge beyond the conventional vision, considering it as a sort of black box. It sheds light on the possibility to further qualify knowledge and provides a former and innovative link between knowledge and complexity. However, the notion of complexity used therein seems to be constrained to a generic definition of an object the elements of which are characterized by a high degree of interaction. The degree of complexity of the system is considered as exogenous, defined ex ante. As an implication, the empirical effort does not go beyond the count of classes and of patents assigned to classes.

The viewpoint of endogenous complexity makes the analysis of knowledge dynamics particularly appealing and challenging. Knowledge can indeed be represented as an emergent property stemming from multi-layered complex dynamics. Knowledge is indeed the result of a collective effort of individuals who interact with one another, sharing their bits of knowledge by means of intentional acts of communication (Antonelli, 2008; Saviotti, 2007). The structure of the network of relationships amongst innovating agents represents, therefore, a crucial factor able to shape the ultimate outcome of knowledge production processes. Collective knowledge so produced stems from the combination of bits of knowledge dispersed among innovating agents. Creativity refers to the ability of agents to combine these small bits of knowledge so as to produce an original piece of technological knowledge. This in turn may be thought about as a collection of bits of knowledge linked to one another. The knowledge base of a firm can therefore be imagined as a network in which the nodes are the small bits of knowledge and the links represent their actual combination in specific tokens. Knowledge in this sense turns out to be an emergent property of complex dynamics featuring the interdependent elements of the system, i.e. the bits of knowledge.

This is a consequence of the collective character of knowledge production, which provides further richness to its dynamics. Such a complex system may be represented as a network, the nodes of which are the smaller units of knowledge, while the edges stand for their actual combination. Hence the knowledge base is characterized by a structure with its own architecture. Learning dynamics and absorptive capacity represent a channel through which the topology of knowledge structure affects search behaviour at the level of agents’ networks. Indeed, agents move across the technology landscape in regions that are quite close to the area of their actual competences.

The empirical efforts related to the endogenous complexity of the knowledge base are based on the exploitation of patent data, but try and use the rich information provided therein. In particular, technological classes are used to investigate the patterns by which they are combined into patent documents and to calculate some key indicators, such as: variety, which points to the differentiation of knowledge; cognitive distance, which can be defined as the distance between the present knowledge base of a firm or a sector and the external knowledge which is the object of the search; and knowledge coherence, which is related to the extent to which the technologies combined together are complementary to one another (Krafft et al., 2014).

3 Knowledge complexity and the systemic approach to innovation technology policy

The recognition of the peculiar characteristics of knowledge and of the complex mechanisms related to the generation and diffusions of innovation has relevant implications for innovation and technology policies. In this respect, a systemic approach appears to be necessary to be adopted in innovation studies and, more specifically, in the analysis and design of innovation policy (Borrás and Edquist, Chapter 14 of this book). As private innovative efforts, technological and institutional capabilities and different public support policies should be accounted for in an integrated manner, the systemic approach suggests analysing the patterns of production and diffusion of innovations by adopting a complex framework of research. This means that private activities, public policies and consumers’ behaviour dynamically co-evolve and define development pathways (Saviotti and Pyka, Chapter 2 in this book).

In this context, a key role in the production and absorption of technological knowledge is played by the existing innovation system as a whole, represented by the industrial, institutional and social framework and the associated physical and knowledge infrastructures (Breschi et al., 2000; Crescenzi, Gagliardi and Percoco, Chapter 8 in this book). Moreover, the systemic perspective allows for the recognition of the important interrelations and complementarities between technological and market forces and different policy instruments (Edquist, 2005; Smits et al., 2010). In this context, both demand and supply forces should be taken into account. In particular, the stock of knowledge and the improvement of technological capabilities through research and development (R&D) activities are found to be very important for production and diffusion innovation at both the micro and the macro levels. In parallel, the extent of market demand and the level of prices have been considered as relevant market incentives to innovative activities (Schmookler, 1966; Mowery and Rosenberg, 1979).

The systemic approach to innovation and technology policy also allows the shortcomings to be overcome of the standard normative economic theory of innovation policy in guiding policy-makers in the design and implementation of effective policy tools (Crespi and Quatraro, 2013). The traditional foundations of innovation policy relates to the correction of market failures by changing the incentives of private sector agents. Market failures may also limit the generation of new technologies because of the public good nature of knowledge and the related problems of appropriability (Nelson, 1959; Arrow, 1962). However, a growing body of economic literature suggests that traditional economic approaches are inappropriate for dealing with the dynamics of structural and adaptive changes in economic systems (Rammel and van der Bergh, 2003), while highlighting the potential of evolutionary economics to interpret the process of economic development and innovation policies (Metcalfe, 1995). According to these contributions, an evolutionary foundation of innovation and technology policies should account for concepts such as adaptive behaviour, policy learning, policy interactions, diversity, path-dependence and lock-in processes. When the generation of new knowledge is conceived as a complex evolutionary process distributed in a system of different agents whose behaviour and interactions are governed both by market forces and by non-market institutions (Metcalfe and Ramlogan, 2005), it clearly appears that agents’ interactions and the institutions governing them...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of figures
• List of tables
• List of contributors
• 1 Knowledge, innovation and the different dimensions of systemic technology policy
• PART I Knowledge, innovation and the demand side
• PART II The supply-side dimensions
• PART III Innovation and systemic technology policy
• Index