Réka Horváth (p), Levente Szász , Ottó Csíki

The paper aims to explore how factors of regional competitiveness are associated with the location of car manufacturing companies in the EU. Although the European automotive market can be characterized by an intense dynamics in terms of location choices, literature offers little empirical guidance on how regional factors influence the location of car manufacturers in the EU. This paper aims to fill this gap by combining regional competitiveness data on 276 EU regions with the actual location of all 269 production units of car manufacturing companies currently present in the EU. Logistic regression is used to discover significant relationships, while the comparative analysis of clusters of regions is meant to offer a more detailed understanding of the role of different location factors. Results of the analysis show that the most influential location factor is related to infrastructural development, but other competitiveness factors, such as regional innovation capabilities or labour market efficiency, might also play an important role.

Enrique Claver-Cortés, Bartolomé Marco-Lajara, Pedro Seva-Larrosa, Lorena Ruiz-Fernández (p), Francisco García-Lillo

Business relocation decisions have been approached from different perspectives within regional science. Specifically, this paper helps to elucidate the costs of the Spanish regions, both in terms of business demographics and turnover, as a result of the relocation decisions of Spanish companies for a 7 years period (2013-2019). To do this, we use a sample of 33,511 companies located in Spain which have moved their headquarters in the period analysed. To address this issue, this paper presents a comparative study between regions, in which it is possible to notice which have been the winner and loser regions, in terms of the analysed variables (business demographics and turnover).
The results obtained from the study allow us to establish several conclusions regarding the effect of business relocation on the different Spanish regions. First, we have studied the relevance of company movements between regions compared to total movements (intra-regional and inter-regional) in a specific geographical context (Spain). Second, we have determined which regions have been net recipients of companies versus those that have experienced a net loss in their business demographics. Finally, we have quantified the net profit/loss of turnover by region that derives from the relocation of companies from one region to another.

Maria Kubara (p)

Location decision plays an important role in firm’s future perspectives, growth and survival. Starting from classical and behavioural theories, through NEG to evolutionary and co-evolutionary approaches the individual decision about where to locate business was modelled. Whereas older theories focused more on optimisation and cost minimisation process, considering complete or bounded rationality, the modern approaches stress the importance of economy of density and externalities, but also interactions with and within the neighbourhood.
Individual choices aggregate in non-trivial spatial organisation patterns of business. Their structure can differ significantly for different industry branches. In the highly innovative fields however the role of spatial organisation is even more crucial and can highly influence the outcomes. Innovation process, highly spatial sensitive and distance decaying, happens most dynamically in the dense business clusters. While different theoretical approaches can be used for modelling the individual location decision, in the innovative field we expect that externalities and interactions will play a crucial role. In overall structure, we may expect visible clusters (possibly in a form of Central Business District), with mostly stable structure. Possible gradient from the unimodal structure could occur. To verify those theoretical predictions the empirical data about technological start-ups from Warsaw was analysed. The spatial structure of business localisation was investigated, using spatial machine learning methods, detecting clusters and their spread. Also, the dynamics of localisation choices in years 2010-2018 were considered. The spatiotemporal analysis of the phenomenon was conducted to investigate changes and (in)consistencies over time and space. Obtained results suggest non-stable spatial pattern in localising new start-ups in Warsaw.