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HOW ITALIAN FIRMS PERFORMANCES ARE INFLUENCED BY INNOVATION OF DOMESTIC AND FOREIGN FIRMS NEARBY IN SPACE AND SECTORS? A SPATIAL ANALYSIS.

This work is in the wake of the work of Ferragina et al. (2011, 2012), Ferragina and Mazzotta (2013; 2014) which explore to what extent the probability of Italian firm’s performance are related to agglomeration economies, to backward and forward production and innovation linkages, to spillovers effects from FDI and from other forms of internationalization, . We adopt a large dataset at firm level built by the authors over the last two years. More specifically, we carried out: 1) merging of Aida data with other administrative dataset from ISTAT providing information on inter firms linkages at local level by measuring input-output relations between firms /industries and at the global level inlcuding also subcontracting, exporting, delocalisation ; 2) measuring of different features of industrial districts; 3) merging with data on innovation provided by other sources (Marin and Lotti for AIDA firms; CIS for sectoral and geographic detailed information on innovation). Hence, firm level data (balance sheet data) are taken from Aida database and matched with data on innovation at a more aggregated level from other sources. The reference period spans from 2002 to 2010. We intend to carry out the analysis upon the following issues. We wish to assess: 1) the geographical and sectorial spillovers on firms’ total factor productivity (TFP) in the Italian manufacturing industry; 2) the existence of potential TFP premiums originating from innovation, properly accounting for spatial distances in place between firms and using the most recent techniques of spatial econometrics. The novelty of our contribution is the attempt to build complex indicators of innovation taking into account spatial and sectorial productivity and innovation spillovers at once. To this purpose, the innovation spillovers are built by adopting matrices of geographical distances and technological exchanges between firms.TFP estimates, at the firm level, are first recovered resorting to the Levinsohn and Petrin (2003) semi-parametric approach, allowing for labour and capital coefficients to vary over branches of economic activity. TFP spillovers are then estimated by means of a spatial autoregressive model, allowing for a complementary analysis of the spatial diffusion of productivity shocks as well as spatial diffusion of innovation efforts. More precisely, geo-referenced data are exploited to detect geographical-based spillovers and input-output matrices for inter-sectorial trade to detect sectorial-based spillovers, both demand-side and supply-side driven. Moreover, data on innovation such as demand for patents and others, precisely localized at firm level, are employed to design a comprehensive indicators of technological space, suitable to catch the presence of indirect knowledge spillovers enhancing productivity. A further refinement of the analysis consists of taking into account not only R&D undertaken by a firm but benefit from innovation carried out by other firms. Since the effect of the flow of knowledge between firms in the same sector may differ from that between firms of different sectors, we disentangle the impact of intrasectoral and intersectoral R&D spillovers. A set of controls is considered in estimation to properly isolate TFP premiums.