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The effects of human capital, R&D and firm’s innovation on patents: a panel study on Dutch food firms

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Abstract

This paper analyzes the determinants of a firm’s patent output using data for 2000–2008 which enables us to take into account the role of workers’ skills, other firm’s innovation (process, product, organization) as well as other important firm characteristics including unobserved firm-level heterogeneity. The main results, robust across specifications, suggest a positive and significant effect of process innovation and workers’ skills on patent counts while product innovation becomes an important determinant in explaining forward citations. The inclusion of unobserved heterogeneity is essential in the analysis and seems to affect our results.

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Notes

  1. For instance, on the role of competition, the seminal paper of Aghion et al. (2005) provide theoretical and empirical support for an inverted-U relationship between innovation (patents) and competition (Lerner index). The explanation of this relationship emphasizes that the escape competition effect tends to dominate for lower levels of competition whereas the Schumpeterian effect tends to dominate at higher levels of competition. However, a recent paper by Correa (2012) finds a negligible inverted U-shape when controlling for structural breaks using the Aghion et al. data.

  2. To the extent that we can still learn much more about the size and competitive effects on innovation, an important finding is that patent propensities increases with firm size significantly, because larger firms can better diversity their fixed costs of patent application over a large number of patents (Nagaoka et al. 2010).

  3. We refer to the CIAA (2010) for cross-country comparison of R&D investment levels and cross -industry comparisons of output innovation across world markets.

  4. The patent data in the study was measured according to the international patent classification that pertains to "food and food stuff", "baking" and "butchering". The other EU countries taken in the analysis include Denmark, France, Germany, Italy, Poland, Spain, the Netherlands, UK and USA.

  5. In the Sect. 4.3. We discuss in more detail the construction for some of the variables (human capital, competition, size).

  6. The materials that follow in this section is heavily drawn from Vancauteren et al. (2015).

  7. See, for example, Amemiya (1985) on Tobit models.

  8. Note, we follow Dustmann and Rochina-Barrachina (2007) and use a probit equation in the first step and an OLS estimation in the second step equation.

  9. For a detailed discussion on zero-inflated count models we refer to Cameron and Trivedi 2013.

  10. We did not include a market share based on sales data because of data constraints. In the Tobit II R&D sample selection equations and the patent equation, we employ variables that are extracted from the “General Business Register” (Statistics Netherlands) and are recorded for each firm located in the Netherlands. Sales data are available from the Statistics of Finance of Enterprises and are only available for a smaller selection of firms.

  11. As shown by Cameron and Trivedi (2013), the total marginal effects of any variable appearing in both \( \lambda_{it}^{{}} \) and \( p_{it}^{{}} \) in respectively Eqs. (6) and (9) is equal to sum of the corresponding component of \( \delta_{3} \) and \( \delta_{4} \), where the latter has to be multiplied by 1−\( p_{it}^{{}} \).

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Acknowledgements

We thank participants of the 146th EAAE Seminar on “Technology transfer as a driver of innovative entrepreneurship in agriculture and the agri-food industry” held in Chania (15-16/072015).

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  1. Hasselt University, Campus Diepenbeek, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium

    Mark Vancauteren

  2. Statistics Netherlands, Heerlen, The Netherlands

    Mark Vancauteren

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Correspondence to Mark Vancauteren.

Appendix

Appendix

See Table 7.

Table 7 Sample means and standard deviations
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Vancauteren, M. The effects of human capital, R&D and firm’s innovation on patents: a panel study on Dutch food firms. J Technol Transf 43, 901–922 (2018). https://doi.org/10.1007/s10961-016-9523-2

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