Abstract
In this paper we assess the relevance of both knowledge creation and diffusion processes in affecting Italian SMEs’ propensity to innovate. In doing so a knowledge production function (KPF) is estimated for a representative sample of small and medium manufacturing firms over the period 1998–2003. To account for endogeneity of R&D effort in the KPF, we estimate a Heckman selection model on R&D decisions. The KPF is estimated for three different samples of firms using a standard probit where the probability that SMEs will innovate depends upon intramural R&D effort, regional and industrial spillovers and a vector of interaction and control variables. The main results obtained are the following: first, being located in the South, although does not affect the firm’s choice of starting R&D projects, affects negatively the amount of R&D investments. Second, the probability to innovate is positively related to sectoral spillovers and the magnitude of such impact is decreasing in firms’ size. Third, knowledge diffusion via geographical proximity enhances the probability of the recipient firm to innovate only if it has an appropriate endowment of human capital.
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Notes
- 1.
When talking about knowledge spillovers we refer to disembodied spillovers as defined by Griliches “[…] ideas borrowed by research teams of industry i from the research results of industry j. It is not clear that this kind of borrowing is particularly related to input purchase flows” (Griliches 1992: S36).
- 2.
Within innovation literature, ‘innovation’ is very much related to ‘knowledge’—that is, the cognitive capabilities to elaborate and develop new ideas. Among the different conceptualisations of innovation, Fisher (2006) conceives innovation as “the application of novel pieces of knowledge or a novel combination of existing pieces of knowledge” (1998:1).
- 3.
We use the 14 sectors provided in our sample as a framework for calculating the sectoral spillover variable (these are: Food & beverage, Clothing, Footwear & leather, Wood & furniture, Paper, Fuel, Chemical products, Plastic products, Mineral products, Metal products, Mechanical products, Electrical equipment including optical instruments, Motor Vehicle and Other sectors). Regional spillovers are calculated using the 19 Italian Regions (Valle D’Aosta and Piemonte are counted as one).
- 4.
Equation (1) assumes a unitary absorption capacity across firms. Our results will show that the ability of a firm to capture available external knowledge increases with its internal endowment of human capital.
- 5.
The eighth wave of Capitalia contains information on 4,680 firms; the ninth wave of Capitalia gathered information on 4,289 firms.
- 6.
The adopted merging procedure and data cleaning is described in detail in the Annex.
- 7.
Traditionally, there are two approaches to measuring innovation outputs: the ‘object’ approach and the ‘subject’ approach. Measures of the first approach range from patent counts and patent citations to new product announcements (recently, new data have been proposed; these are the Literature-based Innovation Output (LBIO) data which are compiled by screening specialist trade journals for new-product announcements—see van der Panne 2007). The second approach focuses on the innovating agent and includes small-scale incremental changes. The most important example of the ‘object’ approach is the SPRU database, developed by the Science Policy Research Unit at the University of Sussex. The CIS (Community Innovation Survey), developed by the European Commission together with Eurostat and DG-Enterprise is one of the most comprehensive ‘subject’ oriented database which attempts to collect internationally comparable direct measures of innovation. For a comprehensive discussion on various measures of innovations see Smith 2006.
- 8.
Human capital endowment is measured as the share of employees with a higher education degree. As for the location of firms, we use a geographical dummy taking the value of 1 for firms located in the South of Italy and zero otherwise. The export orientation dummy is equal to 1 if the firm is involved in export activities and 0 otherwise. Age refers to the years of activity of the firm. The technology degree is measured through a dummy which takes the value of 1 if the firms operates in the high-tech sector (which correspond to the science-based sector in the Pavitt’s taxonomy) and 0 otherwise.
- 9.
As we could have a potential endogeneity of exports, we regressed R&D engagement in 2003 on exports reported in the period 1998–2000 and found that the direction of the link between export and R&D is robust (results available upon request).
- 10.
The South dummy captures the dualistic structure of the Italian economy—the so-called Mezzogiorno and the rest of the country—is probably unique among the countries of the European Union. The structural poverty of the Mezzogiorno economy producing a less-favourable environment (e.g. transport and communications, education, and public order) considerably reduces the technological possibilities of local firms. Indeed, given the uncertainty of the economic system, many Southern entrepreneurs may be reluctant to undertake investment programmes aimed at improving technology and at enhancing their operating scale. This applies in particular to R&D projects.
- 11.
We can notice the negative sign of the estimates of ρ. It indicates that there is a negative correlation between the error term of selection equation and that of the outcome equation. That is, those firms which are more likely to do R&D, invest less in R&D; whereas those firms that are less likely to do R&D, invest more in R&D.
- 12.
The wide innovation-related literature recognizes the importance of investments in machinery and equipment for innovation. Scholars such as Cohen and Klepper (1992, 1996) have argued that large firms rely upon human capital endowments, and physical capital investments to support their innovative activities, whereas innovation among small firms originate from informal learning by doing, by using, and by interacting with suppliers and competitors.
- 13.
Note that the coefficient of regional R&D spillovers does not change sign if aggregating knowledge at provincial level.
- 14.
Recalling the wide literature that studies diffusion of information through social links (Rogers 1995; Valente, 1995; Singh 2003; Morone et al. 2006), in fact, it can be argued that the probability of reporting innovations is highly related to knowledge diffusion only if firms located in the same region are socially well connected. In light of this, we may conclude that firms located in most of the Italian regions, lack sufficiently tight social links. This observation does not hold for all Italian regions, as local contexts differ substantially in terms of social capital endowments (for a survey on the relationship between local endowment and the rising of Italian industrial districts see Becattini 1987).
- 15.
Boschma (2005) provides a comprehensive taxonomy of five forms of proximity (geographical, institutional, social, cognitive and organizational) studying the channels through which they either enhance or hamper knowledge transfers.
- 16.
We wish to thank an anonymous referee for pointing this out.
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Acknowledgements
The authors are grateful to Marco Vivarelli for helpful comments provided on an earlier version of the paper and to Nick von Tunzelmann for precious suggestions and comments. The authors also thank Cesare Imbriani for the helpful discussion provided in different stages of the work. Capitalia data and helpful advice from Attilio Pasetto (Area Studi Capitalia) are gratefully acknowledged. Finally, we wish to thank our two anonymous referees for constructive criticism. The usual disclaimers apply.
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Appendices
Annex: Construction of Panel Data
The eighth and ninth Capitalia surveys cover the periods 1998–2000 and 2001–2003 respectively. The firms included in the surveys were selected by means of a mixed procedure: sample-based for firms with between 11 and 500 employees, and exhaustive for firms with more than 500 employees. The composition of the sample was determined using a random selection procedure stratified by class of employees, location and sectors. Note that the survey design is stratified and rotating, so that about half of the firms in the eighth wave (1998–2000) are dropped in the ninth wave (2001–2003), with other new firms being added. The choice of firms to be dropped from the eighth wave, and of those to be added in the ninth wave was casual, but still aimed at maintaining the stratified nature of the sample. In order to construct our balanced panel data we retrieved information only on those firms present in both waves.
Given this panel data, we proceeded to evaluate the difference in firms’ sectors and size between the balanced panel data and the eighth and ninth waves of Capitalia survey, in order to evaluate if the sectoral and dimensional composition of the initial samples has been respected.
From Table 6, we can notice that the share of firms of our panel is, on average, in line with the one observed in the two Capitalia samples. However, we should mention that the share of firms in the balanced panel data appears to be slightly underestimated in some cases and slightly overestimated in other cases. More precisely, we can observe that our panel, when compared to both Capitalia survey waves, slightly overestimates the share of firms with 21–50 employees and underestimates the share of firms with 251–500 employees. Similarly, our sample overestimates the share of firms located in the north-east and underestimates the share of those located in the south. Finally, firms in traditional sectors are slightly underestimated, whereas those in specialised sectors are overestimated.
All in all, we believe the results reported in Table 6 provide a confirmation of the reliability of our sample.
Cleaning Procedure
Our data cleaning procedure consisted of several different stages. First, to refine the firm’s constitution year variable, which contains several missing values, we compared the information from the Capitalia questionnaire with information gathered from an independent data source (AIDA database). In doing so, we substituted all missing and erratic observations with AIDA information and, in the case of inconsistency, proceeded to report the oldest year of firm’s foundation. The second step was converting into euros the R&D expenditure and the physical capital investment recorded in Italian liras back in 1998.
All mentioned variables were also reported to constant prices by using value added industry output deflators of Southern and Northern areas of Italy (the source of deflator is SVIMEZ). However, the presence of several missing values in most of the relevant variables obliged us to perform our study on a restricted number of observations.
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Morone, P., Petraglia, C., Testa, G. (2013). Looking Around: The Smart Way of Italian SMEs to Innovate. In: Pyka, A., Andersen, E. (eds) Long Term Economic Development. Economic Complexity and Evolution. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35125-9_12
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