Institutional Diversity and Innovation

Cornelia Storz, Sebastian Schäfer

• Publisher: Routledge
• Year: 2011
• ISBN:
  9781136715471
• Edition: 1
• Topic: Volkswirtschaftslehre
• Subtopic: Internationale Wirtschaftswissenschaften

The concept of "innovation systems" has gained considerable attention from scholars and politicians alike. The concept promises not only to serve as a tool to explain sustained economic development, but also to provide policy-makers with scientifically grounded policy options to advance the growth of economies. The thrust of much recent literature has been to review existing empirical findings in order to deduce "best practice" models which are assumed to benefit all countries in a similar fashion. However, as this book argues, such 'universal' models often fail in both analysis and policy prescriptions, as they do not take into account sufficiently the circumstances and development trajectories of particular countries. With a foreword by Richard Whitley, this book discusses the extent to which the diagnoses and reform recommendations of recent work on innovation theory, and the related policy recommendations, actually apply to Japan and China. Making links between behavioural economics and institutional analysis, the book covers their regulatory framework, legal and science system, the labour and capital market, and intra-firm relations. It examines the present design and reasons underlying the Japanese and Chinese innovation systems, and based on those findings, emphasises the necessity for reform to secure the future competitiveness of both countries. The book is introduced by a foreword by Richard Whitley, Professor of Organisational Sociology at Manchester Business School.

1 The nature and measurement of innovation

1.1 Introduction: innovation matters

Today it has become commonplace to view innovation as the main source of economic growth in a modern economy: the more innovative we are the better we are able to compete in world markets (Baumol 2002; Mokyr 1990). This is true for nations and firms alike (Porter 1990). Innovation and technological change are thus seen as critical drivers in achieving high standards of living and of economic well-being; innovation policy and related measures are therefore at the centre of economic policymaking today.

In this chapter we focus on the nature and the measurement of innovation, and present empirical data on Japan’s and China’s innovative capacity. Whilst the ‘new growth theory’ has made enormous progress in allowing us to understand growth-enhancing factors, and has progressed also beyond understanding technology as an exogenous variable (see for an excellent overview Jones and Romer 2010; see also Box 1.1) we rely in this book on the ‘national innovation systems’ and the ‘varieties of capitalism’ approaches. This is first because these, due to their primarily institutional approach, devote considerable attention to the systemic, historically embedded and path-dependent character of technology and innovation, and secondly, since we share with them an interest in the – rather stable – differences in innovative outcomes between countries. These are fields of interest which we share with many others researching on the ‘knowledge society’ (David and Foray 2003), the ‘learning economy’ (Lundvall and Borras 1997, 2005) and the ‘knowledge-based economy’ (Foray and Lundvall 1996).

The discovery of the critical role played by innovation and its institutional embedding in the growth of an economy has spurred much scholarly engagement with this subject. Important contributions to this field stem from the literature on ‘national innovation systems’ (Freeman and Soete 1974; Freeman et al. 1982; Freeman 1987; Lundvall 1992; Nelson 1993), from the ‘varieties of capitalism’ approaches (Hall and Soskice 2001) and from evolutionary economics (Nelson and Winter 1982; Dosi et al. 1988) (for a more comprehensive overview of the relevant literature see Chapter 3).

Moreover, specialized journals have been established such as Research Policy, Industrial and Corporate Change and Economics of Innovation and Technology. The increased interest in innovation is reflected in the increasing number of articles with the term ‘innovation’ in their title (see Figure 1.1).

Figure 1.1 Number of scholarly articles with ‘Innovation’ in the title, 1955–2008 per 10,000 social science articles)

Source: ISI Web of Knowledge, Social Sciences Citation Index (SSCI).

Box 1.1 Determinants of economic growth

One of the most important questions in economics is what causes economic growth and thus prosperity for the people of the world? This question has been investigated during the last decades in economics. In the 1960s, growth theory consisted mainly of the neoclassical model, as developed by Solow (1956), Swan (1956), and Koopmans (1965). One feature of this model is the convergence property: the lower the starting level of real per capita gross domestic product (GDP), the higher is the predicted growth rate. The convergence property derives in the neoclassical model from the diminishing returns to capital and depends on the propensity to save, the growth rate of population, and the position of the production function. In the following decades this standard framework has been augmented by human capital in the forms of education, experience, and health. By including human capital, the rate at which per capita output approaches its steady-state value might converge faster because the adaptation of foreign technologies is facilitated by a large endowment of human capital (see Nelson and Phelps 1966). This implies that a country´s growth rate is more sensitive to its starting level per capital output the greater is its initial stock of human knowledge. However, due to the assumption of diminishing returns of capital, the model predicts per capita growth eventually to cease. This is at odd with empirical studies using long-run data for many countries indicating positive rates of per capita growth that can persist over a century or more. Growth theorists of the 1950s and 1960s recognized this modeling deficiency and usually patched it up by assuming that technological progress occurred in an unexplained (exogenous) manner. This shortcoming was obviously unsatisfactory because longrun per capita growth is determined entirely by the rate of technological growth (innovation) that comes from outside the model.

Beginning with Romer (1986) new growth theory or endogenous growth theory sought to supply the missing explanation of long-run growth. Aghion and Howitt (1992), for example, show that innovation results from purposive R&D activity and this activity is rewarded, along the lines of Schumpeter (1993), by potential monopoly rents. This model shows that growth rates can remain positive in the long run, if there is constant influx of ideas and inventive activity. This class of models suggests that innovation depends primarily on personnel engaged in R&D and the existing knowledge (Romer, 1990; Aghion and Howitt, 1992; Jones, 1995). Whereas endogenous growth theory incorporates insights from the Austrian school of thought like Schumpeter and Mises emphasizing the role of the actor, models in this tradition, however, ignore the role of institutions in the innovation process. Subsequent research has shown that appropriate institutions affect and stimulate the production of knowledge and R&D (Freeman 1987). They facilitate the process of registering new patents, to disseminate ideas and promote cooperation across researchers, to speed up diffusion of scientific knowledge, to improve enforcement of property rights and to reduce the uncertainty of new projects. Hence, economists are increasingly aware that institutional arrangements affect knowledge accumulation (e.g. Sala-i-Martin, 2002) and consequently, institutional arrangements affect the long-run growth of output. Along these lines, models have been developed trying to integrate the institutional environment into neoclassical growth theory. As Tebaldi and Elmslie (2008) note: ‘the long-run growth of the economy is intrinsically linked to institutions and suggests that an economy with institutions that retard […] the utilization of newly invented inputs will experience […] low growth rates’. The recent theoretical and empirical findings from institution-augmented standard growth models and extensions of the endogenous growth theories relate to the literature on (national) innovation systems which is also the working horse in this book (Freeman 1987; Lundvall 1992). This approach focuses on the role of institutions – including macro institutions such as intellectual property rights, labour and capital market-related institutions, and micro institutions such network and firm-related incentives and – and analyzes how institutions channel innovative behavior in the economy.

Sources: Barro (1998); Aghion and Howitt (1992); Tebaldi and Elmslie (2008)

1.2 Innovation: basic concepts

1.2.1 Definition and related terms

In this book, we define innovation as follows: innovation is the combination and transformation of input factors, especially institutions and knowledge, into a novel output within a given system. Combinations of input factors that do not lead to an output are thus not an innovation. In other words, for our understanding of innovation output is a crucial element; however, this output is not necessarily commercially successful. The output can instead be a new idea that solves a distinct problem. We now elaborate in more detail on the various terms of our definition – input factors and institutions, knowledge, novelty, output, system.

1.2.1.1 Input factors, institutions and actors

It is unusual to conceive institutions as an input factor. While research on economic growth and development in the 1940s and 1950s focused primarily on the role of capital for growth, since the 1950s the literature has been enlarged by including additional input factors such labour, technology, skills and structural change. But it is only during last two decades that institutions and entrepreneurship have been added as relevant factors (see Kasper and Streit 1998 for an overview). North (1990) even conceives of institutions as the ‘ultimate cause’ for growth. In this book, we focus on the role of institutions and the way they affect actors’ behaviour and the output, for example, the generation of new knowledge. Besides institutions on the macro and meso layer that affect innovation (e.g. the labour and capital market), it is especially the industrial organization with its distinct actors – large and small firms – that affects the innovative outcome. Distinct institutional features in the industrial organization – such as either the dominant role of large or of small firms – thus strongly affect the outcome in different ways.

1.2.1.2 Knowledge

According to the definition above, knowledge has to be combined and transformed into a novel output in order to become an innovation. Knowledge is thus, at the same time, output (namely of institutions and actors’ learning) and input (of innovation). The connotative term ‘research’ refers to one source of new knowledge, which emerges in intentional processes. Usually, three types of research can be distinguished. Basic research refers to experimental and theoretical work undertaken to acquire new knowledge without looking for long-term benefits other than the advancement of knowledge. Applied research is original work undertaken primarily to acquire new knowledge with a specific application in view. It is undertaken either to determine possible uses for the findings of basic research or to determine new methods or ways of achieving some specific and predetermined objectives. Experimental development refers to systematic work, using existing knowledge gained from research or practical experience for the purpose of creating new or improved products and processes.

What do we mean by knowledge? Here, we refer to two important distinctions in regard to the knowledge base: between implicit and explicit, and between creative cumulative and creative destructive knowledge bases. In colloquial language, knowledge is often associated and used interchangeably with information. However, a basic distinction should be drawn between information and knowledge. While knowledge – in whatever field – empowers its possessors with the capacity for intellectual or physical action and has its roots in people’s brains, information is formed from structured and formatted data that remain passive and inert until used by those with the knowledge needed to interpret and process it (see, for instance, Arrow 1974; David and Foray 2002). Information (also called explicit knowledge) refers to codified and materialized knowledge that becomes detached and independent from the economic actor and hence is measurable in terms of patents, journal articles or technologies. However, in order to use this information and combine it, to understand and develop it further and to apply it to a given problem or business opportunity, we need also implicit knowledge (or non-codified, personal-bound, tacit knowledge). Implicit knowledge refers to those technical and social skills that are acquired through individual and social learning, that is, by experimenting and collaborating with other individuals within and across organizations (Leonard and Swap 2004; see also Box 1.2). Implicit knowledge is especially important in the early phase of innovation (Weinkauf et al. 2004; Leonard and Sensiper 2000). For example, financial investors have to evaluate the economic viability of startups, or managers have to decide whether employees’ projects become implemented or not, and recent research has shown that implicit knowledge significantly affects innovation speed (Knockaert et al. 2009). Hence, explicit and implicit knowledge are interrelated in the process of knowledge creation. Compare, for example, a study on biotechnology which illustrates that especially in new technologies formal contracts are rarely used, and that boundary-spanning social networks are crucial for knowledge generation and exchange (Liebeskind et al. 1996). Due to the inherent difficulties in measuring implicit knowledge, the measurement of the output is largely confined to explicit knowledge. This may lead to a biased view and even underestimate the underlying innovativeness in terms of new skills and techniques which are accumulated during the transformation process.

A further, and for this book important, theme refers to the distinction between different knowledge bases in an economy. Nelson and Winter (1982) differentiate between ‘technological regimes’, and Winter (1984) between ‘knowledge systems’. The ‘creative accumulation regime’ and the ‘creative destruction regime’ are diametrically opposed. The former is characterized by a lower level of opportunities and a higher level of cumulativeness, whereas the latter is characterized by a higher level of opportunities and a lower level of cumulativeness. Industries associated with the creative accumulative regime are the transportation, electronics, machinery or robots industry. With the creative destructive regime, different industries are associated such as packaged software and biotech products. Creative accumulative regimes are characterized by competences that are localized and cumulative in nature. Cumulativeness refers to the idea that actors have to solve a series of related tasks in some sequences, and then, while solving the tasks, speed up learning by using information or knowledge obtained from solving previous tasks (Bharadwaj and Kandwal 2008: 113). Technological cumulativeness thus expresses ‘the degree by which the generation of new knowledge builds upon current knowledge’ (Malerba 2007: 690). In most cumulative industries, coordinative economies like those of Japan (or Germany) possess comparative advantages (Audretsch 1995; Casper and Whitley 2004; Hall and Gingerich 2004; Malerba 2007; Storz 2009). In the so-called creative-destructive regimes, learning via accumulation plays a less important role. Instead, new industries in this regime possess the property of being competence-destroying since they build on scientific bases that differ significantly from the existing knowledge base of established industries (Powell 1996). In these knowledge bases, the USA possesses strong competitive advantages, as will become clear in our empirical data on innovation in section 1.4.

Box 1.2 Learning and knowledge

Firms differ in their management of technology and innovation (in short, innovation management). The locus of knowledge may be different: it may be located outside or inside the firm, and firms may decide to rely more on internal or more on external knowledge (compare basically Malerba and Orsenigo 2000; Thornhill 2006; Yang 2005). Malerba (1992) identifies different types of learning which induce different types of knowledge generation:

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