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EVOLUTIONARY ECONOMIC GEOGRAPHY
An emerging field or framework?
David L. Rigby
Introduction
At a very broad level Economic Geography seeks to understand spatial uneven development, how geographies of growth and decline are produced, how they change over time and what role the spatial distribution of economic activity plays in the processes that drive these dynamics. Since the 1980s, Economic Geography has been restless. For practitioners within this field, the research domain, theoretical cores and principal methodologies have been up for grabs, along with the preferred connections to other fields within geography and those further afar (Martin & Sunley, 1996; Amin & Thrift, 2000; Plummer & Sheppard, 2001; Bathelt & Glückler, 2003). There is little question that the pluralism introduced by various ‘turns’ has broadened Economic Geography. Whether such breadth deepens our leverage over substantive concerns remains to be seen. Against this changing backcloth, a shift to embrace evolutionary ideas, a variant of heterodox economics, stands as one of the most recent examples of the dynamism of the field. While the popularity of Evolutionary Economic Geography (EEG) has grown rapidly, precisely what it offers economic geographers, and whether and how it should be integrated with existing theoretical frameworks are important questions that are beginning to generate healthy debate (Boschma & Frenken, 2006; Grabher, 2009; Mackinnon et al., 2009; Boschma & Martin, 2010; Pike et al., 2016).
This chapter provides a brief overview of the emergence of evolutionary economics and the adoption of evolutionary arguments within Economic Geography. The aim of the chapter is to highlight recent theoretical and empirical research within EEG and to foreground some key emerging debates. The chapter is divided into three sections. The first section briefly reviews the development of Evolutionary Economics (EE) and the competing strands of evolutionary thought that have been embraced by geographers. The second section explores how those arguments have been deployed within Economic Geography. The final section offers a brief conclusion and links many of the themes explored by EEG to a related literature in International Business Studies.
Key perspectives in EE
Within economics, disagreement over the adoption of a mechanistic framework based on Newtonian physics versus the adoption of evolutionary claims resting on a Darwinian logic is longstanding (Mirowski, 1989; Hodgson, 1993). Parts of this ‘debate’ are well-known, from the biological mecca of Marshall (1920), that was stillborn in his concept of the representative agent, through Veblen’s (1898) search for an evolutionary model of social structure based on the ‘natural selection’ of routines or habits of thought, on to Schumpeter’s (1942) model of creative destruction, even though that explicitly eschews links to evolution in a biological sense. However, these concerns did little to slow the ascendancy of what is nowadays referred to as the orthodox model of Neoclassical Economics. The standard macro-form of this framework rests heavily on sets of homogeneous, utility-maximizing agents who are perfectly informed and adjust to exogenous price shocks instantaneously and at zero cost (Kirman, 1992; Hartley, 1997). The impact of such assumptions is well-known, from no real (or at best an under-socialized) interaction between agents, no purposeful decision-making or strategic behavior, no innovation and, in aggregate, no real dynamics.
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Sustained interest in EE developed with the work of Nelson and Winter (1982) and an alternative framework for understanding economic dynamics in an explicitly historical sense. This framework was built around the strategic choices of heterogeneous profit-seeking firms operating under the constraints of bounded rationality in competitive markets (Simon, 1957). Nelson and Winter (1982) constructed models of technological search that were localized by experience and by routinized behaviors that changed slowly over time through learning. Firms, and their associated technologies and routines, were selected by market pressures thus reshaping the environment within which future competition was seen to unfold. In short, Nelson and Winter (1982) outlined a number of the core components of a Darwinian model of evolution within a socio-economic domain and thus laid the foundation for a resurgent EE.
Writing thirty years later, Dollimore and Hodgson (2014) claim that the field of EE has become so fragmented that no over-arching theoretical framework has developed out of the pioneering efforts of Nelson and Winter (1982). This, they contend, is a primary reason why EE has had so little traction within the broader discipline of economics. At least part of the explanation for this theoretical stasis has been internal battles over the core of the field (Hanappi, 1995). Two broad models of EE have been offered to understand the dynamics of processes that influence the behavior of economic agents and the environment within which they interact. The first of these is linked to arguments that we typically associate with modern biology and the evolutionary work of Darwin and Spencer. A second model for EE rests on the principles of self-organization in complex systems. These two models are briefly discussed next.
The Darwinian model of evolution in socio-economic systems rests on the concepts of variety, selection and retention. Variety refers to differences in the characteristics of individual agents, firms and workers that comprise the populations under study. Selection refers to processes through which economic agents with certain characteristics, for example technologies or behavioral routines, increase or decrease their (frequency) weight in the population (Hodgson & Knudsen, 2006). Retention refers to processes through which these characteristics are propagated over time. Within the socio-economic sphere, it is critical to add to these standard evolutionary claims a mechanism that generates novelty and so maintains the heterogeneity over which processes of selection can operate (Witt, 2003; Metcalfe, Foster & Ramlogan, 2006). Learning and experimentation with production technologies, with organizational routines or with institutions that shape individual and/or group behavior serve this purpose (Nelson & Winter, 1982; Arthur, 2014).
Attempts to build a social or economic Darwinism have themselves taken a number of forms (Witt, 2004). Early applications of neo-Darwinism, underpinning crude and dangerous forms of socio-biology (Wilson, 1975; Becker, 1976), looked for a genetic base to explain human behavior. An alternative vision relied on Darwin and Spencer for metaphorical inspiration, often constructing analogies between evolutionary processes in biology and economics (Vromen, 2001; Nelson, 2006). However, the most general model, proposed by Hodgson and Knudsen (2006) and defended by Aldrich et al. (2008), suggests that populations of heterogeneous agents in different domains (natural and social worlds) can be understood as ‘evolving’ through the mechanisms indicated earlier, though the precise form of those mechanisms, the actual processes involved, vary from domain to domain. This is the model of Generalized Darwinism that assumes a common evolutionary ontology across different populations of competing units, and explicitly rejects the notion that socio-economic dynamics should be explained using strictly biological terms. Metcalfe’s (1998) important work is consistent with this view. Witt (2004, 2006) remains a prominent critic.
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An alternative framework for EE is located in models of complex systems and self-organization (Arthur, 1989, 2014; Allen, 1992; Foster, 1997). At root, complex systems are dissipative structures that import energy used by interacting agents to fuel behaviors leading to the emergence of structures or higher-order patterns that, in turn, shape future interaction and behavior. Systems where order emerges export entropy to other systems outside their borders, and linkages between different systems can result in higher-order complexity (Foster, 2005). Complexity science explores these co-evolutionary patterns of self-organization in open systems that are far from equilibrium, where resources are distributed and where interactions are typically non-linear and adaptive (Byrne, 1998; Miller & Page, 2007). There is no single unified theoretical model at the heart of complexity science, research in this field tends to take the form of network models where non-deterministic relationships between agents and an environment are simulated via sets of algorithms, and where scientific discovery focuses on identification of emergent patterns and shifts in the nature of interactions over time. Complex systems are irreducible in that they cannot be understood by examining relationships between their sub-components, and they are evolutionary in the sense that phases of emergence and structural transformation have an explicit time dimension that is irreversible.
Economic systems have long been supposed to exhibit characteristics typical of complex systems. Adam Smith’s (1776) notion of an ‘invisible hand’ and Friedrich Hayek’s (1960) ‘spontaneous order’ that led groups of agents to unintentionally form socially beneficial higher-order structures are perhaps the most well-referenced examples, though whether such structures rest on unregulated self-interest is debated (Sugden, 1989). Thus, technologies, markets and other forms of networks, even patterns of uneven growth over space and time, represent emergent properties in complex systems of socio-economic behavior. Arthur (2014) and Beinhocker (2006) identify the core ideas of complexity economics, and Wilson and Kirman (2016) provide a recent overview. Evolutionary game theory is commonly regarded as distinct from EE proper (Hodgson & Huang, 2012). An excellent review of evolutionary economic modeling with a strong bias toward evolutionary game theory is provided by Safarzynska and van den Bergh (2010).
Evolutionary analysis within Economic Geography
Evolutionary strains within Economic Geography reach back at least as far as the embrace of political economy from the late-1970s and through much of the 1980s. Massey’s (1984) geological metaphor of new rounds of investment, embodying new technologies and institutional forms, deposited on top of existing institutional arrangements interacting to revise forms of capital accumulation and rework economic landscapes, represents an early example. Harvey (1975, 1982) also provides a working out over space and time of the inherent intra- and inter-class dynamics that drive change within the capitalist mode of production. These dynamics are evolutionary in the sense that new structures of accumulation are formed out of existing constraints. Sheppard and Barnes (1990) and Webber and Rigby (1996) attempt to formalize these arguments in what might be considered an evolutionary geographical political economy. Mackinnon et al. (2009) and Pike et al. (2016) make a strong case to strengthen recent evolutionary analysis within Economic Geography by providing it with an explicit political economy motor.
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In the following discussion, theoretical and empirical research within Economic Geography that has an evolutionary flavor is reviewed. To help structure the argument, the core evolutionary principles of variety, retention and selection are employed. This is not to claim that the materials examined speak directly to one or more of these issues, just that they provide a convenient framework to organize ideas. The discussion does not dwell on which of the two broad models of EE are followed by economic geographers. While some have endorsed Generalized Darwinism (Essletzbichler & Rigby, 2007, 2010) and others have experimented with complexity (Frenken, 2000; Plummer & Sheppard, 2006), most remain agnostic. In truth, there is much that is common between these viewpoints. Martin and Sunley (2006, 2007) provide detailed accounts of work in Economic Geography that is consistent with these models, and Boschma and Martin (2010) provide a highly readable overview.
The production and destruction of variety within Economic Geography
As noted in the last section, some form of variety, or heterogeneity, among decision-making agents is critical to evolutionary accounts of (regional) economic dynamics (Saviotti, 1996). That heterogeneity does not have to be assumed, it is likely to emerge in settings where economic agents interact and learn from one another over time in settings where information is incomplete, where rationality is imperfect and where actions are influenced by social structures that are themselves mutable. Those structures may emerge without direction from routinized behaviors at the individual level, in other cases they might be shaped by self-interested agents, or groups thereof, who have amassed the resources and thus the power to direct change (Martin, 2000; Gertler, 2010). Socio-economic spaces emerge through the interactions and choices of different sets of agents located in different places who both make and are subject to varying constraints and opportunities. Spaces are always in transformation, shaped by multi-scalar processes that are historical composites of the agency and the institutional arrangements, some more enduring than others, that they enmesh (Storper & Walker, 1989; Peck & Theodore, 2007).
While these simple arguments are broadly consistent with a vision of economic geographies that are evolving, it is certainly the case that economic geographers have not, until recently, thought carefully enough about the heterogeneity that underpins this vision. Important work in Economic Geography through the 1980s and 1990s, began to provide systematic evidence of regional differences in patterns of industrial specialization (Ellison & Glaeser, 1997), organizational forms (Scott, 1988; Saxenian, 1994), institutional practices (Storper, 1997; Peck & Theodore, 2007; Gertler, 2010), forms of labor organization (Herod, 1998) and technologies (Rigby & Essletzbichler, 1997, 2006). However, there have been relatively few attempts to identify the extent and the form of heterogeneity in any of these substantive fields, how different processes of selection may operate across that heterogeneity, how new forms of heterogeneity are developed and existing forms eliminated, and how variety in any single domain co-evolves with that in other domains (see also Potts, 2000). For some (Metcalfe, 1998), these issues are foundational to evolutionary accounts, guiding the structure of change in any space economy.
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These claims motivated the early research of Essletzbichler and Rigby (2005) to map the nature of variety in production techniques across sectors of the U.S. economy. They used establishment level data for the population of U.S. manufacturing operations to examine how much heterogeneity existed within narrowly defined industries, and how that heterogeneity shifted over time and over space. The influence of selection (differential growth), plant entry and plant exit on aggregate shifts in industry technology were measured, and empirical support was provided for the work of Metcalfe and Gibbons (1986) who argue that the ‘shape’ of heterogeneity within industries directs the pace and direction of technological change. In similar work, Frenken, Saviotti and Trommetter (1999) develop product spaces as representations of variety across industrial sectors. They explore patterns of radical and incremental innovation within these sectors, the emergence of dominant designs and specialization that they relate to niche theory in evolutionary biology.
Frenken, van Oort and Verburg (2007) argue that different forms of variety are important to regional growth. Building on well-known debates around Marshall-Arrow-Romer externalities and Jacobs externalities in the agglomeration literature (Glaeser et al., 1992), Frenken, van Oort and Verburg (2007) developed the concept of related variety to indicate the extent to which different economic activities are related to one another in a technological or market sense. They argued that related forms of variety hold the possibility of dynamic economies of scope, of potential recombination of activities that are neither too close to one another to crowd the market, nor too distant from one another to impede useful interaction, not unlike Noteboom’s (2000) claims about optimal cognitive distance. Unrelated variety, then, refers to activities that are so distant that viable recombination is unlikely. The concept of related variety, measurement issues and the meaning of specialization are discussed further by Essletzbichler (2015) and by Kemeny and Storper (2015).
Related variety as a way of thinking about the nature of heterogeneity and its influence on economic performance, has been given additional power through the work of Hidalgo et al. (2007) who measure the relatedness of products and how the structure of product variety within countries shapes development possibilities. In turn, these ideas have spawned a great deal of research within EEG that explores relatedness and related variety in different settings. For example, Boschma and Iammarino (2009) show that related variety in Italian exports has a positive and significant influence on regional growth and employment, and Quatraro (2010) reveals how knowledge coherence, another form of related variety, is a significant determinant of productivity growth within Italian regions. In other work, patterns of industrial diversification within regions, or regional branching, are explained by the relatedness of existing industrial capabilities to new sectoral growth paths (Neffke, Henning & Boschma, 2011; Boschma, Minondo & Navarro, 2013; Zhu, He & Zhou, 2015). Measures of skill relatedness and firm diversification are examined using Swedish micro-data by Neffke and Henning (2013), and Muneepeerakul et al. (2013) generate occupational relatedness measures for U.S. cities and address how occupational structure regulates the ability of urban labor markets to transform themselves. Within a different domain, and building on earlier work on technological coherence within firms (Jaffe, 1986; Teece et al., 1994) and regions (Graf, 2006), Kogler, Rigby and Tucker (2013), Balland, Boschma and Kogler (2015) and Rigby (2015) use patent data to estimate technological relatedness between knowledge classes and show how the knowledge architectures of different cities guide technological diversification. Kogler, Essletzbichler and Rigby (2016) trace the evolution of technological relatedness across European regions. Important extensions of this research are just beginning to tackle the question of relatedness among institutional structures and how these are distributed over space (Cortinovis et al., 2016).
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Of course, there is a much larger literature on institutions that emphasizes the socio-cultural and political foundations of structured behaviors that help shape what we commonly understand as the economy. This work usually begins with the old institutionalism of Veblen (1898) and his ‘settled habits of thought’. This provides a vision of institutions (rules and conventions) that limit agency and the power of actors to shape the systems within which they operate. In contrast, the new institutionalism of North (1990) and Williamson (1985) privileges a form of agency built around rationality and transaction cost theory from which organizations and institutions emerge. Here agency plays the dominant role. Sociological variants of institutional theory view the economy, a system of commodity production and exchange, as embedded within networks of social relationships through which trust, understanding and power are generated (Polanyi, 1957; Granovetter, 1985). Martin (2000) and Peck (2005) provide excellent overviews of these claims, both offering an understanding of capitalism as a socio-economic system in permanent flux, a complex interacting melange of forms of production and socio-cultural regulations, norms and values that periodically congeal in more or less durable socio-economic structures producing interlinked spaces and scales of economic activity within and across which agents of different kinds make choices that both reinforce and undermine existing socio-spatial arrangements. This vision of a dynamic and spatially variegated capitalism is more recently explored by Peck and Theodore (2007).
Within Economic Geography, models of evolutionary dynamics that stress the importance of institutions have largely been developed as part of the literature on national and regional systems of innovation, on learning regions and localized knowledge economies (Freeman, 1987; Morgan, 1997; Cooke, 2002; Asheim & Gertler, 2005). Across this literature there is broad agreement that the production of knowledge is highly uneven over space, especially that knowledge which is valuable as a result of its complex and tacit character (Maskell & Malmberg, 1999). With knowledge production increasingly imagined as a process of recombining existing ideas in new ways (Kauffman, 1993), that unevenness is explained by spatial variations in the volume and the quality of interaction (Lundvall & Johnson, 1994; Bathelt, Malmberg & Maskell, 2004; Storper & Venables, 2004), which, in turn, are thought to rest upon the development of localized communities of practice (Lawson & Lorenz, 1999), shared values, norms of behavior, and traded and untraded interdependencies that engender trust (Saxenian, 1994; Storper, 1997). The difficulty of replicating institutional formations over space only serves to exacerbate the critical role of geography in knowledge production. While Gertler (1995) reveals the importance of cultural, organizational and geographical proximity in technology adoption and use, Balland and Rigby (2017) show that complex forms of knowledge do not travel well. Boschma (2005) explores the linkages and tradeoffs between different forms of proximity.
This focus on the production of novelty, both in terms of institutions and technologies, is critical to the evolutionary framework, for it is the process by which variety is introduced to the socio-economic system. While the production of new forms of knowledge has been extensively studied within an evolutionary context, along with the links between technological and institutional systems (Freeman & Perez, 1988; Murman, 2003) and their co-evolution (Schamp, 2010), MacKinnon et al. (2009) charge that the production of new institutional forms has been somewhat neglected within EEG. To some extent this may reflect a rather narrow engagement with institutions in evolutionary economics whereby sets of rule-guided behaviors are sampled by economic agents, with profits influencing those practices to be maintained and discarded and thus prompting a search for alternatives (Nelson & Winter, 1982; Loasby, 2002; Hodgson, 2007; Wilson & Kirman, 2016). It is certainly the case that these arguments say relatively little about the processes through which institutions are created, copied and set into competition with one another, how hierarchies of institutions interact and shape behavior, how much variation in agency institutional formations allow and whether this heterogeneity is the source of new constraints on action. Though Boschma and Frenken (2009, 2011) dispute the broad charge of MacKinnon et al. (2009), they do agree that more work is required to understand how firm-level routines become institutionalized at various spatial scales. Research on this question demands that EEG broaden its methodological base, from quantitative analysis of secondary data, through case studies of regions and industries, toward ethnographies of firms and other agents as well as the ‘lives’ of institutions in spatial clusters that evolve over time.
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Path dependence and lock-in as forms of retention in space
Individual agents within the market economy make choices that impact their survival. For example, firms choose products to produce and technologies, routines, organizational forms and locations that they hope will generate the returns to sustain their operations. Firms learn through production and through interacting with other agents. Processes of selection send frequent signals to firms indicating whether they should exploit current practices or whether they should explore new possibilities (March, 1991). However, limited information and steep increases in the cost of search around existing practices, as well as gains from learning and interaction, means that firms and other agents get locked into trajectories of action that are shaped by past decisions (Arthur, 1989, 1994; David, 1985). The concept of path dependence, of an historical inertia that reinforces logics of action over time, perhaps following random shocks or chance events, has exerted a significant influence on how economic geographers have imagined the historical dynamics of spatial uneven development (Grabher, 1993; Martin & Sunley, 2006; Hassink, 2007).
Path dependence is seen as positive, when firms and other agents coalesce around technologies, modes of organization and institutional forms that enhance mutual understanding, interaction, specialization and cost-sharing. The evolution of regional economies is not only path dependent, it is also place-dependent (Martin & Sunley, 2006). Thus, for Arthur (1994), when repeated choices of independent agents begin to favor one location over another, processes of cumulative and reinforcing advantage set in rapidly. Economies might develop around a dominant technological design (Clark, 1985; Anderson & Tushman, 1990), a more limited knowledge platform (Maskell, 2001; Iammarino & McCann, 2006), a shared organizational culture (Saxenian, 1994) or a common institutional or political configuration (Storper, 1997). For Maskell and Malmberg (1999), regional competitive advantage rests on a distinctive set of localized capabilities.
Boschma and Frenken (2011) examine the spatial clustering of economic activity as a path dependent, evolutionary process. They privilege Klepper’s (2007) spinoff model of cluster formation, where successful firms birth spinoffs that also tend to be successful as they replicate the practices of their parents. When this process is concentrated in space, so clusters of related industrial activity emerge. A number of empirical studies provide support for this model (Boschma & Wenting, 2007; De Vaan, Boschma & Frenken, 2012). A more well-known model of cluster dynamics emerges from models of agglomeration. Thus, for Marshall (1920), localization economies are seen as emerging in particular places because of the formation of dense pools of skilled labor, spatially concentrated buyer-supplier networks and localized knowledge spillovers. For Jacobs (1969), urbanization economies are anticipated to flow from diverse industrial clusters that enhance possibilities for recombination. While these arguments are reasonably well-understood, empirical evidence on the relative strength of these two mechanisms remains inconclusive (Beaudry & Schiffauerova, 2009). For Potter and Watts (2011), the nature of agglomeration economies is argued to follow the life-cycle of industries. This claim is consistent with the nursery cities model of Duranton and Puga (2001), who also fit a geography to the life-cycle claims. Neffke, Henning and Boschma (2011) explore the dynamics of agglomeration economies along the life-cycle of industries, and Rigby and Brown (2015) show that new and old firms benefit from agglomeration in different ways.
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Path dependence is negative when these same processes lock firms and other agents into forms of activity that are no longer profitable. For Grabher (1993), functional lock-in (inter-firm relations), cognitive lock-in (common worldview) and political lock-in generated a territorial-industrial trap that prevented the old industrial areas of the Ruhr from restructuring when decline was inevitable. Hassink (2010) investigates the circumstances under which different forms of lock-in have made it more or less difficult for new industrial capacity and growth paths to emerge in different regions. Martin (2010) extends our understanding of path dependency and raises questions that are taken up in accounts of path creation (Simmie, 2012; Dawley, 2014) and path destruction (Glückler, 2007).
While the concepts of path dependence and lock-in are undeniably important to EEG, they do not themselves comprise an independent framework of evolutionary dynamics. Nonetheless, exploration of these terms has forced us to think about the circumstances under which technological, institutional and industrial transformations occur, whether by design or historical accident, how such changes might be related, and what the dynamics of these changes mean for adaptation and the long-run resilience of regional economies (Christopherson, Michie & Tyler, 2010; Simmie & Martin, 2010; Balland, Rigby & Boschma, 2015). Central to much of this work on path dependence and resilience is the creation and destruction of variety, in all its forms, and of the management of that variety within and across regions.
The silence of selection
Though certain aspects of the evolutionary framework have enjoyed considerable attention within geography, selection is not one of them. Essletzbichler and Rigby (2007) offer a standard interpretation of individual selection in a heterogeneous population of competing firms where market choices reward some firms over others. They note that selection does not favor more efficient firms, rather that efficiency allows some firms to better translate revenues into profits and thus increase their weight in the market, along with the technologies and routines that such firms employ. In this way, markets themselves evolve through the actions of individual agents, altering the pressures of competition on remaining firms. Of course, some firms attempt to control the markets in which they operate through scale or product differentiation (Christophers, 2016). Processes of selection, insofar as they reference changes in the distribution of properties belonging to members of a population, are closely connected to the diffusion of those properties within and across populations and to processes of lock-in that periodically interrupt their operation (David, 1985). It is important to distinguish selection from other processes through which population characteristics change. Over the short-run, selection does not influence the characteristics of elements of a population, but it does impact the composition of the population (Hodgson & Knudsen, 2006).
Within socio-economic systems that are distributed over space, these simple observations on selection are significantly complicated, for agents interact in multiple ways and across different populations. Defining the boundaries of populations and how processes of selection work within and across those boundaries is difficult. In simple geographical settings where agents interact with one another over relatively small distances and where such interaction might help denote the boundaries of a region, then competition and selection might only need to be considered at one spatial scale. However, as soon as we admit the interaction of at least some agents across formerly independent regions, at what point do new regions and new markets form and how do processes of selection become established at new or multiple scales? These concerns raise the question of what are appropriate units of selection and whether the region might represent such a unit (MacKinnon et al., 2009). This issue forces us to think about non-market forms of selection that are, perhaps, of more relevance in the transformation of institutions over time and space. Glückler (2007) provides a quite different take on selection developed within a network perspective, but it is unclear how that might help us with some of the questions just raised. Questions of group and hierarchical selection further complicate the conceptual terrain (van den Bergh & Gowdy, 2009; Wilson, 2016).
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Self-organization in networks
There is relatively little research within Economic Geography that explicitly embraces notions of complexity and self-organization. Plummer and Sheppard (2006) provide a prominent exception, adding an explicit socio-spatial dimension to the usual characterization of a complex system in order to examine how structure and agency co-evolve to produce complex, non-equilibrium trajectories of growth within a spatial-economic system. This work is extended by Fowler (2007) and Bergmann (2012) who both explore dynamics in spatial-economic systems that are not driven by equilibrium. Though not developed in explicitly spatial contexts, a series of complexity models has been deployed to understand innovation and the dynamics of technological change within evolutionary frameworks. Frenken (2006) offers a detailed review. Essletzbichler and Rigby (2007) echo Hodgson and Knudsen (2006) in calling for a synthesis of Generalized Darwinism and complexity approaches in EEG to help resolve how emergent structures are selected and how they may adapt within and across socio-economic landscapes. Beinhocker (2011) attempts this integration using information theory.
Within EEG, a rapidly growing corpus of research that embraces many of the concerns with self-organization and complexity theory focuses on networks and their evolution (Glückler, 2007; Ter Wal & Boschma, 2009; Glückler & Doreian, 2016). Following Grabher (2006), this new engagement with networks is less concerned with governance questions than it is with relationality and more formal social network analysis (Bathelt & Glückler, 2003). The vast bulk of this literature focuses on networks of firms and other agents engaged in knowledge production. Contrary to the assumptions of many writing on regional innovation systems, this new work makes clear that most agents within industrial districts or clusters are not ‘linked-in’ to local institutions in the same way. Indeed, the performance of individual firms and other agents is influenced by their centrality in networks and by the content of their network ties (Giuliani & Bell, 2005; Huggins & Prokop, 2017). Network size, structure and openness is shown to impact the production of knowledge within cities and regions (Fleming et al., 2007; Lobo & Strumsky, 2008), and the influence of different forms of proximity on network ties is explored by Broekel and Boschma (2012). Lengyel and Eriksson (2015) broaden this work in their analysis of co-worker networks and regional productivity growth. Morrison (2008) and Breschi and Lenzi (2015) illustrate how gatekeepers translate and regulate flows of knowledge between networks. An enduring theme in much of this research is the interaction between social networks and the spatial juxtaposition of economic activity. Thus, Breschi and Lissoni (2009) raise important questions about the relative strengths of social proximity and spatial proximity in their analysis of knowledge flows prompted by Jaffe et al. (1993).
Following Powell et al. (2005) and Cowan, Jonard and Zimmerman (2007), analysis of network evolution has developed rapidly over the last few years. For example, Cantner and Graf (2006) explore patterns of network entry and exit for innovators in Jena, Germany. Their work highlights the importance of social connections developed through job mobility for understanding collaboration. Balland (2012) investigates how different forms of proximity influence the changing structure of collaboration networks in the global navigation satellite industry, and Balland, De Vaan and Boschma (2012) use a stochastic actor-oriented model to estimate how different mechanisms of tie-formation shift along the industry life-cycle. At larger spatial scales, Cassi, Morrison and Ter Wal (2012) look at how patterns of globalization in wine trade and wine science co-evolve in Old World and New World settings, and Glückler and Panitz (2016) detail how countries shift positions within global value chain networks.
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Conclusion
Those immersed in the wash-spin cycle that is Economic Geography might be forgiven for thinking that they inhabit a frenetic and fractured field that is far from disciplinary norms. A quick glance at the literature in International Business (IB) and management might provide some calming relief. Buckley (2003), for example, asks whether the IB research agenda has run out of steam, and Jack et al. (2008) seek an intervention within the field of International Management which they see as resistant to any examination of its ontological and epistemological foundations. Calls for a re-engagement with history (Jones & Khanna, 2006), with institutionalist logics (Peng, Wang & Jiang, 2008) and with a wider set of methodological approaches (Doz, 2011) in the IB field will sound all too familiar to economic geographers, along with appeals to evolutionary analysis as a framework for understanding firm organization, strategy and IB dynamics (see Kogut & Zander, 1993; Teece & Pisano, 1994; Cantwell, Dunning & Lundan, 2010).
While evolutionary ideas never gained much traction within mainstream economics departments, Dollimore and Hodgson (2014) note that the arguments of Nelson and Winter (1982) ushered in a remarkable period of creativity within the business and management literature. This creativity emerged, at least in part, around the work of Hannan and Freeman (1993) on organizational ecology and a recognition of the diversity of business operations. Attempts to understand that diversity build on the behavioral models of the firm of Cyert and March (1963). They were extended in the resource-based view of Barney (1991) and Wernerfelt (1984) as a platform for sustained competitive advantage, and developed further by Teece et al. (1997) in the form of strategy in dynamic contexts. As in EEG, knowledge production, management and absorption play central roles in the emergence of diversity and its strategic exploitation (Cohen & Levinthal, 1990; Kogut & Zander, 1992). These same concerns animate key research themes in the IB literature with its focus on activities that cross national borders (Kogut & Zander, 1993; Teece, 2014; Cano-Kollmann et al., 2016).
The linkages between recent work in (Evolutionary) Economic Geography and the IB literature should be clear. However, to date there has been relatively little interaction across these fields (though see Beugelsdijk, McCann & Mudambi, 2010), even in the work on globalization, global value chains and production networks (Dicken, 2004; McCann & Mudambi, 2005). This is surprising given shared substantive interests and, to some extent, shared methodologies. While it would be fair, perhaps, to say that research methods within Economic Geography are broader than those found across the IB literature, there is much more methodological correspondence between those working in EEG and in IB. A deeper engagement of these two fields is likely to yield mutual benefits. Let me provide a few brief examples.
First, mirroring Economic Geography, the adoption of an evolutionary approach within IB is increasingly seen as critical to understanding the co-evolution of firms, in this case multinational enterprises (MNEs), and the (multi-scalar) institutional environments that they help produce (Volberda & Lewin, 2003; Dunning & Lundan, 2008; Cantwell et al., 2010). For both IB and EEG, the interaction between economic agents, institutions and other features of the markets in which they operate are critical concerns. However, the creation of institutions, how they vary over space and whether the rise and fall of formal and informal institutional structures rest on processes of selection that resemble those in markets for goods and services are questions that remain not well understood (see Christophers, 2016). More detailed ethnographies of firm operations and decision making in particular local and national contexts would be especially useful to remedy these shortcomings. Furthermore, combining the traditional strengths of IB and EEG research at different spatial scales might lead to interesting findings regarding the geographical extent and the mobility of institutions.
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Second, in both IB and in EEG, as well as in Economic Geography more generally, the analysis of how businesses organize, distribute and control or shape their networks of relationships over space requires improved data (Zander, 2002). This is most evident in IB where the use of gross import and export statistics, often at the national level, distorts our perception of the nature and the extent of economic activity distributed across sub-national spaces (Poon & Rigby, 2017). Value-added trade data and more enterprise-level business statistics are critical to unpack who does what and where. Within EEG too, value added trade data might issue significant correctives to the simple visions of national product spaces and sets of capabilities that are identified using gross trade flow data. How might our understanding of the dynamics of such spaces and the “development potential” of different countries shift as a result?
Finally, across IB and EEG literatures, knowledge production and its management are seen as playing an ever more critical role in the production of competitive advantage within a world economy that is increasingly flat across a number of important dimensions, yet stubbornly differentiated across others (Mudambi, 2008; Boschma & Frenken, 2011; Cano-Kollmann et al., 2016). For scholars of IB and EEG, how firms manipulate and connect the productive potential of economic agents, organizations and institutions across the fragmented knowledge landscapes that they are best adapted to exploit are key questions (Almeida, 1996; Cantwell & Vertova, 2004). Combining the literatures from both fields, we have made important progress in identifying the ways in which knowledge bases vary over space, about what kinds of knowledge are locked in place and those that are more mobile. We have begun to explore how firms source knowledge from different locations and how place is used to protect novelty. However, we know a lot less about the dynamics of knowledge cores at different spatial scales, about the value of different knowledge components and the characteristics of those places that are best able to recombine them in productive new ways. Further recombination of insights from IB and EEG scholars should help us with these questions too.
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