Agglomeration economies, local cluster, and foreign direct investment in Italy

Although FDI inflows into Europe have been substantially rising over the last decade, Italian regions accounted for a very small portion of such increase. Recent empirical studies attempted to understand the reasons for such a slugging performance and pointed out the relevance of some “country-specific” characteristics such as the (in)efficiency of the bureaucratic system and the (mal)functioning of the judicial system (Bronzini, 2004; Guerin and Manzocchi, 2006). Along with these country-specific characteristics it was also underlined how the lack of lawfulness and not necessarily the inflexibility of the labour market and the heavy tax burden - might help understanding the reason why such regions are so ineffective in attracting FDI (Basile et al., 2005; Barba Navaretti, et al.,2009).

Along with country-specific characteristics and the availability of tangible resources, the recent literature seeking to examine inward FDI attractiveness has begun to focus on the importance of agglomeration economies. This, however, has been limited largely to considering the importance of the geographical proximity in production activities, neglecting a wider literature that has looked at more complex forms of firm clustering like local production systems or industrial districts (Menghinello et al., 2010).

The focus of this paper is on the specific characteristics of Italian local production systems, which might attract MNE. These include both institutional characteristics and location-specific factors – often intangible in their nature –, which shape the local production system. With reference to this latter aspect, the immobile and embedded nature of tacit or uncodified knowledge (that cannot be disseminated outside a cluster) should be considered as a key factor in the attraction of FDI.

Hence, a key research question of our study will be to understand whether the cluster's specific characteristics do play a positive role in attracting MNEs or preventing international investors from localising in Italy. In order to address this research question, we shall thoroughly investigate the role of clusters in the process of technology transfer and human skills upgrading, singling out the key characteristics that might attract/hinder the localisation of foreign direct investments. Our baseline model is a discrete choice model that will be estimated using a probit model. The probability that a foreign-owned firm locates in a cluster is determined by a set of explanatory variables. Our set of explanatory variables includes foreign-cluster-based knowledge spillover (i.e. knowledge spill-over by other foreign-owned firms located in the cluster), domestic-cluster-based knowledge spillover (i.e. knowledge spill-over by domestic firms located in the cluster) and other location-specific variables. In order to construct the explanatory variables related to knowledge spillovers we should first identify clusters and then choose a proxy for stocks of knowledge. For the identification of clusters, we refer to the definition proposed by ISTAT (1997), which defines industrial districts as the travel-to-work area level. For the choice of a proxy for the stock of knowledge, we use cumulative firm’s R&D expenditure. More precisely, we measure domestic and foreign knowledge spillovers as total knowledge available to firm i coming from both other foreign-owned and domestic firms located in the cluster.