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Small Business Economics

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24.05.2019

Local knowledge composition and the emergence of entrepreneurial activities across industries: evidence from Italian NUTS-3 regions

verfasst von: Alessandra Colombelli, Gianluca Orsatti, Francesco Quatraro

Erschienen in: Small Business Economics | Ausgabe 2/2021

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Abstract

This paper investigates how the characteristics of the local knowledge bases, stemming from the accumulation and recombination of competences over time, spur the emergence of new entrepreneurial activities across industries at the Italian provincial level. To do so, we exploit information on the number of new firms appearing across diverse industries in each Italian NUTS-3 region (province) from 1997 to 2009 as an indicator of local entrepreneurial activity. To build indicators of both local technological knowledge stocks and local technological knowledge compositions, we collect information on patenting activity at the provincial level. Our findings suggest that the availability of local knowledge spillovers is not sufficient per se to trigger the creation of new firms. Indeed, looking at the properties of local knowledge bases, the rate of new firm formation appears to be higher in contexts featured by knowledge stemming from search activities shaped by the accumulated competences and dispersed across a wide area of the technology landscape. This suggests that, in Italy, entrepreneurship is mostly related to the exploitation of technological knowledge accumulated over time rather than to profiting from radical breakthroughs.

Vorheriger Artikel Cultural diversity and knowledge in explaining entrepreneurship in European cities

Nächster Artikel Knowledge-based service economy and firm entry: an alternative to the knowledge spillover theory of entrepreneurship

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See Vivarelli (2013) and Quatraro and Vivarelli (2015) for an extensive review of the determinants of entry dynamics and post-entry performances.

See, for example, the special issue appeared in Small Business Economics in May 2011, titled “Entrepreneurial Dynamics and Regional Growth”

Bae and Koo (2008) and Bishop (2012) provide empirical investigations in this direction.

Italian NUTS-3 regions correspond exactly to Italian province administrative divisions. The Italian NUTS-3 classification changed in 2006 and 2009, when four and three new provinces were added, respectively. To ensure coherence in the dataset, we use the before 2006 classification. This poses a problem only with respect to the Barletta-Andria-Trani Province, which gathers together seven municipalities that were previously part of the Bari Province and three municipalities that were part of the Foggia Province. Data at the municipality level are not available so that we cannot solve this problem.

It is well-known that these statistics show some limitations insofar as only firms reaching a certain threshold level in terms of size are required to register for VAT. This problem, common to all large datasets, can be overcome only by implementing dedicated surveys, at the cost of reducing geographical coverage. A second shortcoming is related to the fact that a new registered firm may simply be an already existent subsidiary of a foreign company that starts its activity in Italy. In this case, even if the business is new for the local context analyzed, the firm is not a new constituted entity. Unfortunately, we are not able to distinguish this case from the case in which a completely new business is created. However, MNE localization choices are driven, amongst other factors, also by local characteristics related to the availability of local knowledge and to the composition of its structure. This should mitigate, at least partially, this possible bias affecting our measure of new firm creation.

However, we do not deny that local markets are not homogenous with respect to size and that this can introduce some biases in our results. For this reason, as we specify below, we introduce the provincial level of employment amongst the control variables.

This section and the related Appendix 1 extensively builds on Krafft et al. (2014), Colombelli et al. (2013), and Quatraro (2010).

Patel and Soete (1985) use a similar approach.

The limits of patent statistics as indicators of technological activities are well-known. The main drawbacks can be summarized in their sector-specificity, the existence of non-patentable innovations, and the fact that they are not the only protecting tool for R&D investments. Moreover, the propensity to patent tends to vary over time as a function of the cost of patenting, and it is more likely to feature large firms (Pavitt 1985; Griliches 1990). Nevertheless, previous studies highlighted the usefulness of using patents as a measure of new knowledge production. Such studies show that patents represent very reliable proxies for knowledge and innovation outcomes, as compared to analyses drawing upon surveys directly investigating the dynamics of process and product innovation (Acs et al. 2002). Besides the debate about patents as an output rather than an input of innovation activities, empirical analyses showed that patents and R&D are dominated by a contemporaneous relationship, providing further support to the use of patents as a good proxy of technological activities (Hall et al. 1986).

In the calculations, we use four-digit technological classes.

It must be stressed that to compensate for intrinsic volatility of patenting behavior, each patent application is made last 5 years to reduce the noise induced by changes in technological strategy.

A complete description of the implemented local knowledge measures is provided in Appendix 1.

Results do not differ significantly from our preferred specifications and are available upon request by the authors.

Tables 8, 9, 10, 11, and 12 in Appendix 2 report the results of the baseline estimations and further robustness checks. Precisely, presents the results of the estimations when we maintain the same structure as the regressions presented in the main text but estimating our models on the entire sample (i.e., without differentiating between Pavitt sectors). These regressions show the positive impact of higher local knowledge availability on firm formation. Importantly, they signal for the importance of local knowledge variety, while knowledge coherence seems to negatively affect firm creation. Finally, technological cognitive distance seems to not significantly affect firm formation. Tables 9, 10, 11, and 12 present instead the results for the sector-specific estimations when we exclude the main control variables described in Sect. 3.2.3. Relying on these latter estimations, AIC and BIC statistics suggest that adding control variables as we do for the main estimates significantly increases the robustness of our models.

For the sake of clarity, the region and time indexes are omitted.

For Engelsman and van Raan (1991), this approach produces meaningful results particularly at a “macro” level, i.e., for mapping the entire domain of technology.

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Titel: Local knowledge composition and the emergence of entrepreneurial activities across industries: evidence from Italian NUTS-3 regions
verfasst von: Alessandra Colombelli
Gianluca Orsatti
Francesco Quatraro
Publikationsdatum: 24.05.2019
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 2/2021
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-019-00192-3

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