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Regional Innovation Policy

Ron Boschma

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Abstract

INTRODUCTION Why some regions grow more than other regions is a key question in economic geography. Up until the late 1980s, neo-classical economic approaches argued that technology is a key determinant of regional growth. However, these approaches treated technology as an exogenous factor, leaving the geography of innovation unexplained. Inspired by Schumpeter, economic geographers took the lead in criticising this view. Since the early 1980s, they have focused attention on the explanation of the geography of innovation: some regions are more capable of developing and implementing innovations, and region-specific characteristics (including institutions) may be underlying forces. This led to the claim that regions are drivers of innovation and growth. During the last decades, new concepts like industrial districts (Becattini 1987), clusters (Porter 1990), innovative milieux (Camagni 1991), regional innovation systems (Cooke 2001), and learning regions (Asheim 1996) have been launched to incorporate this view. Many of these concepts have drawn inspiration from evolutionary economics (Nelson and Winter 1982; Dosi et al. 1988; Boschma et al. 2002). In a nutshell, an evolutionary approach argues that “the explanation to why something exists intimately rests on how it became what it is” (Dosi 1997: 1531). The objective of this chapter is to outline how evolutionary economics may provide inputs for regional innovation policy. This is not an easy task, since distinctive strands of thought in evolutionary economics hold opposing views on policy. For example, the neo- Schumpeterian approach (associated with Nelson and Winter, among others) advocates an active role for policy makers, while the Austrian approach (such as Hayek) does not (Wegner and Pelikan 2003). Complexity thinking in evolutionary economics takes a policy view that is again very different. Notwithstanding these different views, we will outline some policy recommendations that incorporate recent thinking in evolutionary economic geography (Boschma and Lambooy 1999). This chapter is structured as follows. A brief and selective literature review is given in section 2, providing a theoretical and empirical background for the remaining part, which addresses policy implications. In section 3, we claim that system failures should be taken as the point of departure to underpin regional innovation policy. In section 4, we discuss how history should be taken seriously in regional innovation policy. What is essential to recognise is that history determines not only the policy options that are at hand in regions, but also the probable outcomes of regional innovation policy.

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INTRODUCTION Why some regions grow more than other regions is a key question in economic geography. Up until the late 1980s, neo-classical economic approaches argued that technology is a key determinant of regional growth. However, these approaches treated technology as an exogenous factor, leaving the geography of innovation unexplained. Inspired by Schumpeter, economic geographers took the lead in criticising this view. Since the early 1980s, they have focused attention on the explanation of the geography of innovation: some regions are more capable of developing and implementing innovations, and region-specific characteristics (including institutions) may be underlying forces. This led to the claim that regions are drivers of innovation and growth. During the last decades, new concepts like industrial districts (Becattini 1987), clusters (Porter 1990), innovative milieux (Camagni 1991), regional innovation systems (Cooke 2001), and learning regions (Asheim 1996) have been launched to incorporate this view. Many of these concepts have drawn inspiration from evolutionary economics (Nelson and Winter 1982; Dosi et al. 1988; Boschma et al. 2002). In a nutshell, an evolutionary approach argues that “the explanation to why something exists intimately rests on how it became what it is” (Dosi 1997: 1531). The objective of this chapter is to outline how evolutionary economics may provide inputs for regional innovation policy. This is not an easy task, since distinctive strands of thought in evolutionary economics hold opposing views on policy. For example, the neo- Schumpeterian approach (associated with Nelson and Winter, among others) advocates an active role for policy makers, while the Austrian approach (such as Hayek) does not (Wegner and Pelikan 2003). Complexity thinking in evolutionary economics takes a policy view that is again very different. Notwithstanding these different views, we will outline some policy recommendations that incorporate recent thinking in evolutionary economic geography (Boschma and Lambooy 1999). This chapter is structured as follows. A brief and selective literature review is given in section 2, providing a theoretical and empirical background for the remaining part, which addresses policy implications. In section 3, we claim that system failures should be taken as the point of departure to underpin regional innovation policy. In section 4, we discuss how history should be taken seriously in regional innovation policy. What is essential to recognise is that history determines not only the policy options that are at hand in regions, but also the probable outcomes of regional innovation policy.

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INTRODUCTION Why some regions grow more than other regions is a key question in economic geography. Up until the late 1980s, neo-classical economic approaches argued that technology is a key determinant of regional growth. However, these approaches treated technology as an exogenous factor, leaving the geography of innovation unexplained. Inspired by Schumpeter, economic geographers took the lead in criticising this view. Since the early 1980s, they have focused attention on the explanation of the geography of innovation: some regions are more capable of developing and implementing innovations, and region-specific characteristics (including institutions) may be underlying forces. This led to the claim that regions are drivers of innovation and growth. During the last decades, new concepts like industrial districts (Becattini 1987), clusters (Porter 1990), innovative milieux (Camagni 1991), regional innovation systems (Cooke 2001), and learning regions (Asheim 1996) have been launched to incorporate this view. Many of these concepts have drawn inspiration from evolutionary economics (Nelson and Winter 1982; Dosi et al. 1988; Boschma et al. 2002). In a nutshell, an evolutionary approach argues that “the explanation to why something exists intimately rests on how it became what it is” (Dosi 1997: 1531). The objective of this chapter is to outline how evolutionary economics may provide inputs for regional innovation policy. This is not an easy task, since distinctive strands of thought in evolutionary economics hold opposing views on policy. For example, the neo- Schumpeterian approach (associated with Nelson and Winter, among others) advocates an active role for policy makers, while the Austrian approach (such as Hayek) does not (Wegner and Pelikan 2003). Complexity thinking in evolutionary economics takes a policy view that is again very different. Notwithstanding these different views, we will outline some policy recommendations that incorporate recent thinking in evolutionary economic geography (Boschma and Lambooy 1999). This chapter is structured as follows. A brief and selective literature review is given in section 2, providing a theoretical and empirical background for the remaining part, which addresses policy implications. In section 3, we claim that system failures should be taken as the point of departure to underpin regional innovation policy. In section 4, we discuss how history should be taken seriously in regional innovation policy. What is essential to recognise is that history determines not only the policy options that are at hand in regions, but also the probable outcomes of regional innovation policy.

Key concepts: Economic geography, Regional science, Key (lock), Regional geography, Geography, Economy, Human geography, Political science

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