Social network analysis attracts increasing attention in economic geography. We claim social network analysis is a promising tool for empirically investigating the structure and evolution of inter-organizational interaction and knowledge flows within and across regions. However, the potential of the application of network methodology to regional issues is far from exhausted. The aim of our paper is twofold. The first objective is to shed light on the untapped potential of social network analysis techniques in economic geography: we set out some theoretical challenges concerning the static and dynamic analysis of networks in geography. Basically, we claim that network analysis has a huge potential to enrich the literature on clusters, regional innovation systems and knowledge spillovers. The second objective is to describe how these challenges can be met through the application of network analysis techniques, using primary (survey) and secondary (patent) data. We argue that the choice between these two types of data has strong implications for the type of research questions that can be dealt with in economic geography, such as the feasibility of dynamic network analysis.
This paper applies methods of multiple resolution map comparison to quantify characteristics for 13 applications of 9 different popular peer-reviewed land change models. Each modeling application simulates change of land categories in raster maps from an initial time to a subsequent time. For each modeling application, the statistical methods compare: (1) a reference map of the initial time, (2) a reference map of the subsequent time, and (3) a prediction map of the subsequent time. The three possible two-map comparisons for each application characterize: (1) the dynamics of the landscape, (2) the behavior of the model, and (3) the accuracy of the prediction. The three-map comparison for each application specifies the amount of the prediction’s accuracy that is attributable to land persistence versus land change. Results show that the amount of error is larger than the amount of correctly predicted change for 12 of the 13 applications at the resolution of the raw data. The applications are summarized and compared using two statistics: the null resolution and the figure of merit. According to the figure of merit, the more accurate applications are the ones where the amount of observed net change in the reference maps is larger. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of models using scientifically rigorous, generally applicable, and intellectually accessible statistical techniques.
It is widely accepted that firms in peripheral regions benefit to a lesser extent from local knowledge spillovers than firms located in agglomerations or industrial clusters. This paper investigates the extent to which innovative firms in peripheral regions compensate for the lack of access to local knowledge spillovers by collaborating at other geographical scales. So far, the literature predominantly suggests that collaborations complement rather than compensate for local knowledge spillovers. Using data on the collaboration patterns of innovative firms in Sweden, this paper provides evidence that firms with low access to local knowledge spillovers tend to collaborate more. This effect, however, depends on firm size and in-house capabilities. Our findings suggest that firms with strong in-house capabilities do indeed compensate for a lack of local knowledge spillovers with collaborations while firms with weaker in-house capabilities depend more on the regional knowledge infrastructure.
Europe¿s rural areas are expected to witness massive and rapid changes in land use due to changes in demography, global trade, technology and enlargement of the European Union. Changes in demand for agricultural products and agrarian production structure are likely to have a large impact on landscape quality and the value of natural areas. Most studies address these changes either from a macro-economic perspective focusing on changes in the agricultural sector or from a local perspective by analyzing recent changes in landscapes for small case studies. This paper describes a methodology in which a series of models has been used to link global level developments influencing land use to local level impacts. It is argued that such an approach is needed to properly address the processes at different scales that give rise to the land use dynamics in Europe. An extended version of the global economic model (GTAP) and an integrated assessment model (IMAGE) are used to calculate changes in demand for agricultural areas at the country level while a spatially explicit land use change model (CLUE-s) was used to translate these demands to land use patterns at 1 km2 resolution. The global economic model ensures an appropriate treatment of macro-economic, demographic and technology developments and changes in agricultural and trade policies influencing the demand and supply for land use related products while the integrated assessment model accounts for changes in productivity as result of climate change and global land allocation. The land use change simulations at a high spatial resolution make use of country specific driving factors that influence the spatial patterns of land use, accounting for the spatial variation in the biophysical and socio-economic environment. Results indicate the large impact abandonment of agricultural land and urbanization may have on future European landscapes. Such results have the potential to support discussions on the future of the rural area and identify hot-spots of landscape change that need specific consideration. The high spatial and thematic resolution of the results allows the assessment of impacts of these changes on different environmental indicators, such as carbon sequestration and biodiversity. The global assessment allows, at the same time, to account for the tradeoffs between impacts in Europe and effects outside Europe.
Differences in entrepreneurial activity and entrepreneurial attitude are substantial and persistent across nations and regions. However, studies on entrepreneurship that encompass regions and countries at the same time are lacking. This paper explains both national and regional differences in entrepreneurial attitude and activity for 127 regions in 17 European countries, based on Global Entrepreneurship Monitor (GEM) data. We reveal the importance of institutional factors and economic and demographic attributes to variations in regional entrepreneurial attitude and activity. Our findings point at the relevance of distinguishing between components of entrepreneurial attitudes, i.e. fear of failure in starting business, perceptions on start-up opportunities and self-assessment of personal capabilities to start a firm. We find different determinants of these components, suggesting that they reflect different aspects of entrepreneurial attitude. In explaining regional prevalence rates of phases in entrepreneurial activity (nascent, baby business, established business) we find significant contributions of entrepreneurial attitude components. Urban regions and regions with high levels of nearby start-up examples show relatively high rates of early-stage entrepreneurship. A large number of start-up procedures does not discourage early-stage entrepreneurship.
We analyse inter-regional research collaboration as measured by scientific publications and patents with multiple addresses, covering 1316 NUTS3 regions in 29 European countries. The estimates of gravity equations show the effects of geographical and institutional distance on research collaboration. We also find evidence for the existence of elite structures between excellence regions and between capital regions. The results suggest that current EU science policy to stimulate research collaboration is legitimate, but doubt the compatibility between EU science policy and EU cohesion policy.
Nanomaterials are seen as a key technology for the twenty-first century, and much is expected of them in terms of innovation and economic growth. They could open the way to many radically new applications, which would form the basis of innovative products. As nanomaterials are still in their infancy, universities, public research institutes and private businesses seem to play a vital role in the innovation process. Existing literature points to the importance of knowledge spillovers between these actors and suggests that the opportunities for these depend on proximity, with increasing distance being detrimental to the extent that spillovers can be realised. Due to the technological complexity, however, proximity could also be less important as relevant nanomaterials research is globally dispersed. Hence in this paper, we analyse the effects of co-location of R&D activities on nanomaterial patenting. Based on European Patent Office data at the German district level (NUTS-3), we estimate two negative binomial models in a knowledge production function framework and include a spatial filtering approach to adjust for spatial autocorrelation. Our results indicate that there is a significant positive effect of both public and private R&D on the production of nanomaterial patents. Moreover, we find a positive interaction between them which hints at the importance of their co-location for realising the full potential of an emerging technology like nanomaterials.
We analyze information and knowledge transfer in a sample of 16 German regional innovation networks with almost 300 firms and research organizations involved. The results indicate that strong ties are more beneficial for the exchange of knowledge and information than weak ties. Moreover, our results suggest that broker positions tend to be associated with social returns rather than with private benefits.
A burgeoning body of the literature has studied the migration of university-bound students and university graduates in developed countries, but little research has been conducted on this issue in China. Using microdata from the 2005 1 % population sample survey, this paper examines, for the first time, the migration of university entrants and graduates in China by describing their migration patterns and modeling their choices of destination location. The migration patterns show that recent university graduates are highly concentrated in three eastern provincial units, Beijing, Shanghai, and Guangdong, and that the destinations of university entrants tend to be more dispersed geographically. The results from conditional logit models indicate that highly educated youths, in particular those who study in a regular university, have a strong tendency to stay in the same province after graduation. The migration of university entrants is determined mainly by regional differences in university enrollment, while the distribution of national key universities, economic opportunities, and the cost of living plays a less important role in their location choices. The migration of university graduates is driven primarily by regional differences in wage levels. Comparing with vocational college entrants, regular university entrants are attracted to regions with more national key universities. Comparing with vocational college graduates, regular university graduates are attracted to regions with higher wage levels. Our findings suggest that increasing labor market returns is a more effective approach than investing in higher education to curb brain drain in China’s less developed regions.
This study compares the spatial characteristics of industrial R&D networks to those of public research R&D networks (i.e. universities and research organisations). The objective is to measure the impact of geographical separation effects on the constitution of cross-region R&D collaborations for both types of collaboration. We use data on joint research projects funded by the fifth European Framework Programme (FP) to proxy cross-region collaborative activities. The study area is composed of 255 NUTS-2 regions that cover the EU-25 member states (excluding Malta and Cyprus) as well as Norway and Switzerland. We adopt spatial interaction models to analyse how the variation of cross-region industry and public research networks is affected by geography. The results of the spatial analysis provide evidence that geographical factors significantly affect patterns of industrial R&D collaboration, while in the public research sector effects of geography are much smaller. However, the results show that technological distance is the most important factor for both industry and public research cooperative activities.
Policy makers have identified the relationship between entrepreneurship and economic development. Yet, little is known about how this relationship varies over time in cities with different market sizes. This study examines the link between entrepreneurship and economic development using a panel of 127 European cities between 1994 and 2009. We found that the immediate economic development impact of new firm start-ups is positive for both small-/medium-size cities and large cities. The relationship is U-shaped for large cities, with the indirect effect taking 7 years, but the indirect effect does not occur in small-/medium-size cities. We offer useful information for policy makers, practitioners, and scholars.
In this paper, we investigate the determinants of entrepreneurial activity in a cross section of German regions for the period 1998–2005. Departing from the knowledge spillover theory of entrepreneurship, the focus of our analysis is on the role of the regional environment and, in particular, knowledge and cultural diversity. Our main hypothesis is that both, knowledge and diversity, have a positive impact on new firm formation. As the determinants of regional firm birth rates might differ considerably with respect to the necessary technology and knowledge input, we consider start-ups at different technology levels. The regression results indicate that regions with a high level of knowledge provide more opportunities for entrepreneurship than other regions. Moreover, while sectoral diversity tends to dampen new firm foundation, cultural diversity has a positive impact on technology oriented start-ups. This suggests that the diversity of people is more conducive to entrepreneurship than the diversity of firms. Thus, regions characterized by a high level of knowledge and cultural diversity form an ideal breeding ground for technology oriented start-ups.
This study claims that policy makers may not be sufficiently aware of the importance of maintaining an appropriate balance between exploration and exploitation networks for small and medium-sized enterprises (SMEs). On the basis of the open innovation model, policy makers are also increasingly stimulating SMEs to develop their exploration skills. In the Netherlands, a government subsidy called the ‘innovation voucher programme’ was introduced to stimulate SMEs to develop innovation in cooperation with knowledge institutes. Yet, although many studies show that SMEs tend to have a higher R&D productivity than larger firms, and innovative SMEs are more likely to make external networks with other SMEs or institutions such as universities, there is still little examination of the successfulness of SME’s innovation activities. The growing policy attention for the role of SMEs in innovation prompts the questions how innovation in SMEs can be facilitated, and which factors contribute to the success (or failure) of their innovation efforts. This study explores the innovation strategy of innovative Dutch SMEs by means of their sources of innovation, innovation capabilities, innovation performance, and commercialization sources. By means of structural equation modelling of a sample of 243 Dutch SMEs, this study shows that exploring (technology) opportunity together with institutions such as universities and private research establishments is important for successful innovation in SMEs. But, in addition, our model shows that contacts with competitors are also important for successful innovation performance. Our finding that openness of open innovation also applies to the commercialization phase is too often neglected by researchers and policy makers.
After defining the concept of resilience and its application to the regional context, the paper presents a preliminary evaluation of regional economic resilience in the case of the Italian regions. In doing so, we follow the approach by Martin (J Econ Geogr 12:1–32, 2012) and Martin and Sunley (2015) who identify three different dimensions to regional economic resilience: (a) resistance, i.e., the degree of sensitivity or depth of reaction of a regional economy to a recessionary shock; (b) recovery, i.e., the speed and magnitude of the recovery; (c) reorientation and renewal, i.e., the ability of a region to adapt in response to the shock and renew its growth path. The analysis is conducted at the local labor systems (LLS) geographical level and focuses, at this stage, only on the first two dimensions of resilience, i.e., resistance and recovery. The recessionary shock (2009–2010) is defined following the Italian National Statistical Institute approach for which a recession implies a decrease in GDP for three consecutive trimesters. The pre-recessionary period is 2007–2008 and the recovery period 2011 (as a new recession started again in Italy at the end of 2011). The results clearly point at very heterogeneous resilience for the Italian LLS.
Starting in 2007–2008, an economic crisis with no comparable precedent after WWII has affected most of the World, and Europe in particular. Yet, despite the pervasiveness of the crisis, its impact was highly differentiated across countries. The macroeconomic country-level effects are very important, but also within countries the impact on the various regions has been far from uniform, with some regions, often the most urban, able to resist the crisis better than others. Among the many factors which can have influenced the differential impact of the crisis in Europe, this paper looks at the regional endowment of structural territorial assets, those which have been labelled as “territorial capital”. Territorial capital comprehends all those assets, being material or immaterial, public or private, which represent the development potential of places. Territorial capital enhances regional growth in ordinary times, and, being structural, can be expected to also act as a factor of resilience in times of crisis. To investigate this hypothesis, a database of territorial capital indicators for all regions of the European Union at NUTS3 level is exploited, and a classification of regions based on the endowment of territorial capital is built. It appears that regions belonging to different groups, i.e. being differently endowed with territorial capital, have had different degrees of resilience, with some being able to maintain their income levels better than their country and others losing ground. The structure of regions is hence an important determinant of how they can afford periods of distress, and in particular, more resilient have been those regions endowed with less mobile territorial capital assets and with those territorial capital assets of mixed levels of materiality and rivalry.
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 hownetworks 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.