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Innovation, industry and firm age: are there new knowledge production functions?

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Abstract

In this paper we investigate how the knowledge production function is at work in different industrial sectors comparing mature and young companies in Italy. We estimate a two-step model using community innovation survey data. We provide evidence that young firms are particularly effective in translating R&D into product innovation in ‘entrepreneurial sectors’ (especially in services where it is likely that capital requirements and experience are negligible), while mature companies turn out to be more effective in translating technological acquisitions (TAs) into process innovation in ‘routinized sectors’ (especially in low-tech manufacturing industries where the main strategy is cost reduction).

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Notes

  1. Some studies have already focused on the relative disadvantage of Europe as regards the birth and growth of young innovative companies at the beginning of the XXI century. In this respect, EU start-ups face higher barriers to entry, innovation and growth compared to their US counterparts (Bartelsman et al. 2004, Philippon and Véron 2008).

  2. However, innovation is a multifaceted phenomenon and different types of innovation might determine various consequences at the firm-level (Expósito and Sanchis-Llopis, 2019).

  3. Historically—partly due to the lack of other measures of innovation—most studies mainly focused attention on the determinants of R&D activity and its link with a measure of innovative output, most notably patents. Overall, patents appear to be a very rough proxy of innovation as suggested by several studies (Levin et al. 1987; Patel and Pavitt 1993).

  4. They can only partially depict the overall phenomenon and—as dummies—they suffer obvious limitations.

  5. The lack of a proper panel dimension in our data (see Sect. 6) prevents us from controlling possible endogenous self-selection of firms into an R&D or TA regime, with respect to product and process innovation. See Gkypali and Tsekouras (2015), for an interesting analysis connected to the relationship between export and R&D activities in Greek low-tech manufacturing firms.

  6. Griffith et al. (2006) find that in several European countries, firm size significantly affects the probability of engaging in R&D, but not the level of R&D investment.

  7. These data are not available in CIS4.

  8. As innovative intensities larger than 50% might be considered suspicious and unreliable, we opted for dropping the 8% of the whole sample due to the exceeding of this threshold. However, we also re-run, for robustness check, the analysis proposed in Sect. 7 including these potential outliers and results did not turn out to be significantly different from the ones presented (results are available from the authors upon request).

  9. Unfortunately, given the lack of unique firms’ identifiers, it was not possible to match the two waves in order to have a panel dataset. We opted for using these two contiguous waves as the following one, CIS5, was collected later than expected involving a reduced amount of companies and asking a limited number of questions. More recent data were not available when we decided to focus on this research topic.

  10. According to the European Commission’s (2006) State Aid rules, Young Innovative Companies are defined as being less than 6 years old, among other requirements. However, in adopting the European Directive some European countries have extended this threshold (i.e. 8 years for France and Estonia). The choice of 8 years allows us to reach a good degree of representativeness of the sub-sample of young firms, without increasing the age threshold too much. However, we performed several robustness checks, assuming the alternative thresholds of 6 and 10 years; results—available upon request—are consistent, both in terms of sign and statistical significance of the estimated coefficients, with those discussed in Sect. 7.

  11. Sectoral classification is based on the 2-digit ATECO codes. To a large extent the Italian industrial classification codes (ATECO) correspond to the European NACE taxonomy. To aggregate the industry categories in accordance with the 2-digit NACE classification, we follow Griffith et al. (2006).

  12. Our estimations are based on cross-sectional data, and most of the regressors used are simultaneously determined; therefore interpretation of the results has to be undertaken with caution.

  13. -Business group: belonging to a business group might help promoting and supporting innovation as benefits might come from an easier access to group internal financial resources (Piga and Vivarelli, 2004). However, the drawback might be the lack of autonomy which could affect the possibility to freely select the innovative investments.

    -Appropriability: the well-known availability of different instruments for achieving a greater degree of appropriability of the innovation rents, such as patents, trademarks, secrecy, etc. (protection tools) (Levin et al. 1987) should positively affect the innovative performance. Nevertheless, patenting process is an expensive procedure and its cost might prevent some categories of firms from adopting it (van Pottelsberghe de la Potterie and Danguy 2010).

    -Internationalization: global competition can spur innovation, while static and technologically inactive firms risk to be excluded from the international arena (Narula and Zanfei 2003).

    -Size: following the Schumpeterian Mark II notion, large firms should be more likely to undertake and succeed in innovation. Nevertheless, in the last few decades mixed empirical evidence has been found to support the Schumpeterian hypothesis (Breschi et al. 2000).

    Complete description of the variables and correlation matrix are presented in the Appendix. Looking at the correlation coefficients, the largest value—among regressors—is 0.50, below the risk of multicollinearity issue.

  14. Both the high values of the correlation coefficients (Rho) between the selection and the main equations and the statistical significance of the Lambda Mills (see the lower parts of Tables 2 and 3) confirm the validity of the choice of this Heckman-Type specification.

  15. From the lower part of Tables 3 and 4, it emerges clearly that the two equations are always highly correlated via the errors terms, the level of the rho ranging between 0.43 and 0.61. This aspect, which suggests the existence of a certain degree of complementarity between the two innovative outputs, supports the adoption of a Biprobit model.

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Authors and Affiliations

  1. EPFL (École Polytechnique Fédérale De Lausanne), CDM ITPP IIPP, ODY 2 01.1 (Odyssea), Station 5, 1015, Lausanne, Switzerland

    Gabriele Pellegrino

  2. Università Cattolica del Sacro Cuore, Piacenza and Milan, Italy

    Mariacristina Piva

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  1. Gabriele Pellegrino
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Appendix

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See Appendix Tables 6 and 7.

Table 6 The variables
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Table 7 Correlation matrix
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Pellegrino, G., Piva, M. Innovation, industry and firm age: are there new knowledge production functions?. Eurasian Bus Rev 10, 65–95 (2020). https://doi.org/10.1007/s40821-019-00129-6

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