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There is consensus among scholars and policy makers that knowledge is one of the key drivers of long-run economic growth. It is also clear from the literature that not all knowledge has the same value. However, too often in economic geography and cognate fields we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. In this article we measure the complexity of knowledge, we map the distribution and the evolution of knowledge complexity in US cities, and we explore how the spatial diffusion of knowledge is linked to complexity. Our knowledge complexity index rests on the bimodal network models of Hidalgo and Hausmann. Analysis is based on more than two million patent records from the US Patent and Trademark Office that identify the technological structure of US metropolitan areas in terms of the patent classes in which they are most active between 1975 and 2010. We find that knowledge complexity is unevenly distributed across the United States and that cities with the most complex technological structures are not necessarily those with the highest rates of patenting. Citation data indicate that more complex patents are less likely to be cited than less complex patents when citing and cited patents are located in different metropolitan areas.

Although informal knowledge networks have often been regarded as a key ingredient behind the success of industrial clusters, the forces that shape their structure and dynamics remain largely unknown. Drawing on recent network dynamic models, we analyze the evolution of business and technical knowledge networks within a toy cluster in Spain. Empirical results suggest that the dynamics of the two networks differ to a large extent. We find that status drives the formation of business knowledge networks, proximity is more crucial for technical knowledge networks, while embeddedness plays an equally important role in the dynamics of both networks.

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less--complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy. Balland et al. use data on scientific papers, patents, employment and GDP for 353 metropolitan areas in the United States to show that economic complexity drives the spatial concentration of productive activities in large cities.

We investigate the micro-connectivity drivers of network change in an underperforming industrial cluster in Argentina. Our analysis is based on data collected in two consecutive surveys, conducted in 2005 and 2012, of entrepreneurs in the electronics cluster in Córdoba. We find that social and institutional factors influence micro-connectivity choices at the local level, while firms that are more open to non-local knowledge have the tendency to behave like external stars, potentially limiting the flow of non-locally generated knowledge into the cluster network as it grows. We interpret these results using the intuitions from strain theory and suggest that strain may engender an ‘everyone for themselves’ mentality in the most open cluster firms as they seek to escape from a condition of underperformance. We posit, also, that local social and institutional ties are relevant for most cluster firms to survive, but are not sufficient for the cluster to thrive.

In this article, we study the formation of network ties between firms along the life cycle of a creative industry. We focus on three mechanisms that drive network formation: (i) network endogeneity which stresses a path-dependent change originating from previous network structures, (ii) five forms of proximity (e.g. geographical proximity) which ascribe tie formation to the similarity of attributes of firms and (iii) individual characteristics which refer to the heterogeneity in the capabilities of firms to exploit external knowledge. The article employs a stochastic actor-oriented model to estimate the – changing – effects of these mechanisms on the formation of the interfirm network in the global video game industry from 1987 to 2007. Our findings indicate that, on average, the direction of the effects of the three mechanisms are stable over time, but that their weights change with the degree of maturity of the industry. To an increasing extent, video game firms tend to prefer to partner over short distances and with more cognitively similar firms as the industry evolves.

It is clear that technology is a key driver of economic growth. Much less clear is where new technologies are produced and how the geography of U.S. invention has changed over the last two hundred years. Patent data report the geography, history, and technological characteristics of invention. However, those data have only recently become available in digital form and at the present time there exists no comprehensive dataset on the geography of knowledge production in the United States prior to 1975. The database presented in this paper unveils the geography of historical patents granted by the United States Patent and Trademark Office (USPTO) from 1836 to 1975. This historical dataset, HistPat, is constructed using digitalized records of original patent documents that are publicly available. We describe a methodological procedure that allows recovery of geographical information on patents from the digital records. HistPat can be used in different disciplines ranging from geography, economics, history, network science, and science and technology studies. Additionally, it is easily merged with post-1975 USPTO digital patent data to extend it until today. Design Type(s) data integration objective • database creation objective Measurement Type(s) geographic location Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)

The importance of network structures for the transmission of knowledge and the diffusion of technological change has been recently emphasized in economic geography. Since network structures drive the innovative and economic performance of actors in regional contexts, it is crucial to explain how networks form and evolve over time and how they facilitate inter-organizational learning and knowledge transfer. The analysis of relational dependent variables, however, requires specific statistical procedures. In this paper, we discuss four different models that have been used in economic geography to explain the spatial context of network structures and their dynamics. First, we review gravity models and their recent extensions and modifications to deal with the specific characteristics of networked (individual level) relations. Second, we discuss the quadratic assignment procedure that has been developed in mathematical sociology for diminishing the bias induced by network dependencies. Third, we present exponential random graph models that not only allow dependence between observations, but also model such network dependencies explicitly. Finally, we deal with dynamic networks, by introducing stochastic actor-oriented models. Strengths and weaknesses of the different approach are discussed together with domains of applicability the geography of innovation studies.

Urban outputs, from economy to innovation, are known to grow as a power of a city's population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world's two largest economies (the United States and China) to explore whether a city's position in the networks of communication, human mobility, and scientific collaboration explains variance in a city's patenting activity that is unaccounted for by its population. We find evidence that models incorporating intercity connectivity outperform population-based models and exhibit stronger predictive power for patenting activity, particularly for technologies of more recent vintage (which we expect to be more complex or sophisticated). The effects of intercity connectivity are more robust in China, even after controlling for population, GDP, and education, but not in the United States once adjusted for GDP and education. This divergence suggests distinct urban network dynamics driving innovation in these regions. In China, models with social media and mobility networks explain more heterogeneity in the scaling of innovation, whereas in the United States, scientific collaboration plays a more significant role. These findings support the significance of a city's position within the intercity network in shaping its success in innovative activities.

The role of networks in innovation processes has become a key research area in the field of innovation studies over the last decade and a half (Freeman 1991; Powell et al. 1996; Hagedoorn 2002; Owen-Smith and Powell 2004; Ahuja et al. 2009). Not surprisingly, the rapid increase in the number of studies on innovation networks in an inter-disciplinary field, such as innovation studies, has led to a great variety of theories and concepts (Ozman 2009). Only recently, economic geographers have jumped on the study of the spatial dimensions of social networks in innovation processes (Ter Wal and Boschma 2009), following the literature on national and regional innovation systems developed in the 1990s (Freeman 1987; Nelson 1993; Cooke et al. 1998; Cooke 2001). Despite this attention, theoretical accounts of spatial networks are still underdeveloped (Grabher 2006; Sunley 2008; Hess 2008). This is also true for an evolutionary approach to knowledge and innovation networks, although attempts have been undertaken more recently (Giuliani and Bell 2005; Powell et al. 2005; Cantner and Graf 2006; Sorenson et al. 2006; Boschma and Ter Wal 2007; Giuliani 2007; Glückler 2007; Morrison 2008; Suire and Vicente 2009; Balland et al . 2010; Breschi et al. 2010; Cassi and Plunket 2010; Glückler 2010; Graf 2010; Balland 2011; Broekel and Boschma 2011; Ter Wal 2011; De Vaan 2012).

Do scientific capabilities in regions translate into technological leadership? This is one of the most pressing questions in academic and policy circles. This paper analyzes the matching of scientific and technological capabilities of 285 European regions. We build on patent and publication records to identify regions that lie both at the scientific and technological frontiers (strongholds), that are pure scientific leaders, pure technological leaders, or just followers in 18 domains. Our regional diversification model shows that local scientific capabilities in a domain are a strong predictor of the development of new technologies in that domain in regions. This finding is particularly relevant for the Smart Specialization policy because it implies that the analysis of domain-specific scientific knowledge can be a powerful tool to identify new diversification opportunities in regions.