Carles Méndez-Ortega (p), Josep-Maria Arauzo-Carod

This paper analyses location patters of Software and Videogames industries at the metropolitan area of Barcelona using microgeographic data. These industries benefit from agglomeration economies, skilled labour and, generally speaking, spillover effects, and tend to cluster at bigger metropolitan areas, but less is known about their detailed location patterns inside these areas. We contribute to empirical literature by identifying how Software and Videogames industries firms concentrate in some core areas of the metropolitan area. Our empirical application includes using Nearest Neighbour Index (NNI) and M-functions, as well as local spatial autocorrelation indicators.

Ferhan Gezici (p), Burcu Müderrisoğlu

Clustering adds different dimensions to agglomeration economies just as the change of technology and spatial patterns of networks. From the beginning of 1990s until today, the formation and evolution process of clusters have identified based on different aspects with empirical studies. While Porter (1990) has highlighted the concept of competitiveness, Brusco (1982) and Becattini (2002) have highlighted the externalities based on the socio-economic relations. Furthermore Boschma (2005) has discussed proximity fact in different perspectives, while Wolfe and Gerther (2004) put emphasis on leader firms. The factors and the role of the actors would be different according to sectoral and local characteristics, therefore it still needs to explore dynamics of clusters based on different localities and sectors.
The recent contributions to the cluster literature have been very much related to the global economy, as the local conditions and connectivity to the other parts of the world should be considered for the evolution of the clusters. On the other hand, out of the firm scale, capital, local market control, and easy access to global networks and knowledge, capability of leadership are defined as the leader firms characteristics (Andersson et al, 2004). Morrison (2004), studied on the South Italy furniture cluster and put forward that some big scale firms create information channels and promote the cluster development by their networks with international firms and organizations. Randelli and Lombardi (2014) express the role of leader firms on cluster evolution in Italy leather manufacturers cluster. Therefore, leader firms are central actors who empower the local networks and global pipelines (Lorenzoni and Badenfuller, 1995; Rugman and D’Cruz, 2000). In fact, Randelli and Lombardi (2014) denote that leader actors serve as a central role (hub) by cross-referring to Markussen’s cluster typologies (the hub-spoke cluster).

In the scope of this paper, the role of leader firms in cluster formation and evolution would be investigated on a traditional furniture cluster located in Istanbul Metropolitan Area. Leader firms are identified based on the membership database of Istanbul Chamber of Industry and interviews in the cluster. Within the scope of findings, firms able to access the knowledge by imitation and supplier links to the leader firms. Therefore the position of leaders in horizontal network has become more important in the dispersion of knowledge by promoting global links. On the other hand, the medium size firms, which are not identified as leader firm, also have strong export relations and organisation capacity.

Miguel Gómez de Antonio (p), Stuart Sweeney (p)

Recent research on statistical methods for industrial location, and specifically the analysis of co-location, has demonstrated the advantages of using Gibbs models of spatial point processes. Published work to date has focused on the theory, method, simulation results, and an application to the telecommunications industry in Madrid, Spain. The present paper extends the methods and results for marked spatial point process in Euclidean space or on a road network. The extension allows for different levels of attraction or repulsion among different categories of industry. This could include attraction/repulsion to different sectors, to different employment size classes, or both. All models will also be conditional on "environmental" covariates -- such as distance to labor pools.