Abstract
The analysis of social interactions as drivers of economic dynamics represents a growing field within the economics of complexity. Social interactions are a specific form of interdependence whereby the changes in the behavior of other agents affect utility functions for households and production functions for producers. In this paper, we apply the general concept of social interactions to the area of the economics of innovation and we articulate the view that knowledge interactions play a central role in the generation of new technological knowledge so that innovation becomes the emergent property of a system, rather than the product of individual actions. In particular, we articulate and test the hypothesis that different layers of knowledge interactions play a crucial role in determining the rate of technological change that each firm is able to introduce. The paper presents an empirical analysis of firm level total factor productivity (TFP) for a sample of 7,020 Italian manufacturing companies observed during the years 1996–2005. This will enable us to identify the distinctive role of regional, inter-industrial and localized intra-industrial knowledge interactions as distinctive and significant determinants, together with internal research and innovation efforts, of changes in firm level TFP.
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Notes
We acknowledge that entry can exert a relevant impact on innovation and technological change at the industry level. As witnessed by Aghion et al. (2004), a non-negligible share of productivity growth of incumbents can be attributed to an enhancing effect exerted by (foreign) entrants. This is true under the assumption that entrants immediately locate at the technological frontier. This amounts to an emphasis on the role of incumbents with respect to entrants: this seems consistent with the basic intuition that the decision to innovate is made according to the expectations about the behavior of competitors. However, the core research question in this paper addresses the role of regional and cognitive proximity in shaping knowledge interactions that affect the generation of new technological knowledge. Nevertheless, we recognize that the inclusion of market entry will be a relevant future extension of our analysis tacking an even broader set of research questions.
Industries are defined according to the Italian classification system ATECO. We have adopted a three-digit level. In some circumstances, we had to aggregate data at two-digit level in order to have a sufficient number of firms for a statistically significant identification of the parameters of production functions.
The level of yearly depreciation of physical capital has been chosen following the approach applied in previous studies that have applied perpetual inventory techniques to estimate yearly fixed capital levels adopting depreciation parameters in the range 5–10 % for physical capital.. Since the adopted depreciation parameter is constant across industries, we should not expected changes in the significance of estimate coefficients for slight changes in δ.
R&D expenditures are the traditional indicator used to measure the amount of efforts to generate new technological knowledge. Actually, R&D statistics measure only a partial amount of the overall effort that firms make to introduce new technologies. Internal learning activities are not accounted for, nor is the cost to access external knowledge. Moreover the actual efficiency of the research activities is not considered as, of course, R&D activities only measure partially some inputs into the process. Additional issues that are specific to the Italian institutional and empirical evidence need to be considered. The Italian manufacturing industry is characterized by the geographical clustering of many small firms in specialized industrial districts. There are only a few large firms that represent a minority by all viewpoints. Reliable statistical evidence on R&D expenditures is missing. Official R&D statistics are based upon data collected from only 2,200 agents (be they firms or research organizations). As a consequence, official R&D statistics provide a picture of the research activities conducted by a minor portion of the economic activity carried out in the country. Small firms do not reply to the detailed and time-consuming questionnaires that are used as the indispensable tool for the collection of R&D data that are not requested for the compilation of annual reports. Accountancy rules coupled with fiscal allowances, however, provide excellent and reliable evidence upon stocks of intangible capital that include capitalized research expenditures as well as purchasing costs for patents and licences and the costs incurred to build and to implement brand and know how. It seems appropriate to rely upon the figures publicly available in all annual reports to get a reliable measure of the efforts to generate new technological knowledge.
In the econometric analysis, we have used different definition for the indicator of intangible intensity, using both book values and perpetual inventory approaches with depreciation rates for intangible assets equal to 15 % and 20 %. Results are not significantly affected. In the paper, we present the results based on the yearly ratio of intangible to tangible assets based on book values.
Following a consistent tradition in the applied econometrics of technological spillovers, we rely on data at the firm level, taking into account the main location of each firm (Mairesse and Cuneo 1985; Cincera 1997). This procedure is consistent with the empirical evidence considered: the dataset is based on information extracted from the annual reports of single companies. The average size is small. Multi-plant companies usually operate the different units by means of different legal identities. Hence, each unit of information can be considered mono-plant.
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Acknowledgements
The authors acknowledge the financial support of the European Union D.G. Research with the Grant number 266959 to the research project ‘Policy Incentives for the Creation of Knowledge: Methods and Evidence’ (PICK-ME), within the context Cooperation Program / Theme 8 / Socio-economic Sciences and Humanities (SSH) in progress at the Collegio Carlo Alberto and the University of Torino, and the research assistance provided by Federico Caviggioli. Giuseppe Scellato acknowledges the funding of the Politecnico di Torino. Both authors acknowledge the comments of two anonymous referees and of the editor.
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Antonelli, C., Scellato, G. Complexity and technological change: knowledge interactions and firm level total factor productivity. J Evol Econ 23, 77–96 (2013). https://doi.org/10.1007/s00191-012-0299-8
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DOI: https://doi.org/10.1007/s00191-012-0299-8