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The role of Science Parks: a puzzle of growth, innovation and R&D investments

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Abstract

This paper investigates the impact of Science Parks on growth and innovativeness of affiliated firms. Both the role and the effectiveness of these policy instruments in sustaining tenants’ performances are still open issues. We rely on unique Italian data and compare the performance of firms located within a park with a control sample of off-park firms. Firstly, we focus on the estimation of the treatment effect of being located within the park. Our results show a significant difference between on and off park firms with respect to their innovativeness and propensity to invest in R&D; to the contrary, firms’ growth appears unaffected by location effects. Secondly, we analyse what features of Science Parks drive the impact on the diverse measures of performance. Our main finding is that both patenting activity and R&D investments are actively sustained by the presence and quantity of research centres within the park. Growth remains a largely unexplained phenomenon and Gibrat law is found to hold robustly within the class of on-park firms.

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Notes

  1. IASP International Board, 6th February, 2002.

  2. www.nbia.org.

  3. For an updated mapping of these structures an interesting resource is the website of the POLics project: http://www.polics.it/en.

  4. A list of all parks belonging to such an association can be found here: http://www.apsti.it. The two excluded members are not parks but entities coordinating consortia of institutions including regional administration offices, the national research councils and, eventually, parks themselves.

  5. Only partially completed questionnaires had been disregarded.

  6. Notice that all firms in our dataset are Italian and we used the national ATECO classification which, at the 1-digit level, corresponds exactly to the NACE Rev.2.

  7. This is the case of firm size and growth, in accordance with the literature, but also of innovation-related variables like patent applications and R&D growth.

  8. It is measured as the shortest path in terms of kilometers along main infrastructures (roadways and railways) between the address of firms’ headquarters and that of the park the firm is affiliated to.

  9. Note that we are removing big companies (above 900 employees) from our sample, thereby reducing the risk of including multinational firms.

  10. Notice that industrial traditions and firms performances vary considerably passing from Northern to Southern Italian regions and that our sample is unevenly distributed both across regions and sectors.

  11. The sole observation we were not able to match was unique in its category.

  12. Identified at 2-digit NACE Rev. 2 level.

  13. The interested reader can check and explore in more details what the features discussed in this paragraph looking at Table  12 in the “Appendix”.

  14. In this discussion we focus on sample averages for two reasons: they can be considered as summaries since results are unaffected in a year by year analysis and, secondly, these variables will be used in subsequent cross-section analysis due to the impossibility of building up a longitudinal dataset (see next sections for further explanations).

  15. Not reported for sake of brevity. Normality is tested for all our variables, log-normality is tested for growth rates. In particular we used QQ-plots, Shapiro–Wilk and Jarque–Bera tests.

  16. See Wilcoxon (1945) and Mann and Whitney (1947).

  17. Classic examples of estimation of treatment effects might be found in Heckman and Robb (1985) and Hotz et al. (2006).

  18. This evidence supports the idea that firms with closer ties to Universities achieve better performances; see for example Siegel and Wessner (2012) and references therein.

  19. See Sect. 2.1 and Bianchini et al. (2014), for a recent result.

  20. Promoting joint R&D is among SPs’ objectives (see Sect. 2.2 and also Plaeksakul 2011 for an example) and the search for partners is facilitated in sectorally focused SPs.

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Authors and Affiliations

  1. Institute of Economics, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56127, Pisa, Italy

    Francesco Lamperti

  2. CRIOS - Bocconi University, Milan, Italy

    Francesco Lamperti, Roberto Mavilia & Simona Castellini

  3. MEDAlics, Research Centre for the Mediterranean Relations, Reggio Calabria, Italy

    Francesco Lamperti, Roberto Mavilia & Simona Castellini

  4. University for Foreigners “Dante Alighieri”, Reggio Calabria, Italy

    Roberto Mavilia

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  1. Francesco Lamperti
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  2. Roberto Mavilia
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Correspondence to Francesco Lamperti.

Appendix

Appendix

1.1 Firms growth and park location

Here we briefly present results about the investigation of the relationship between on-park location and growth of firms. It is confined to the Appendix since it only provides additional evidence on what is already suggested in the paper. As for the study of R&D determinants our estimation strategy relies on OLS and a set of quantile regressions that are presented both for the full sample and the set of on-park firms. Table  10 presents the results. It is immediate to see that our treatment variable is not significant in any of the proposed specifications. This evidence confirms findings obtained in Sect. 4: being located within parks’ premises does not affect average growth.

Table 10 Innovation and firms’ growth (pooled)
Full size table
Table 11 Innovation and firms’ growth (on-park)
Full size table

Table  11 provides results about the association between SPs’ features and firms’ growth. Provided that we focus on the same relationship of interest, the use of a quantile regression as estimation technique relies is in line with many recent papers presented in Audretsch et al. (2014) and a recent work by Bianchini et al. (2014). We find a substantial absence of direct effects played by SPs’ features: none of them turns out to be significant in any specification. In addition, indirect effects are as inconsistent as direct ones. In contrast with Bianchini et al. (2014) R&D investments, which are positively correlated with on-park research centres, do not produce any relevant impact on firms’ growth, and innovativeness appears largely unrelated to growth as well. We only see a marginal negative effect of patents in the OLS specification, whose robustness is not credible given the dimension of standard errors and that it disappears in any quantile regression estimation. Firms growth appears then a largely unexplained phenomenon even for firms located within Science Parks.

1.2 On and off park firms shares in the distribution of growth rates

Table  12 shows the share of firms belonging to the treatment and control group that fall in the top and bottom quartile of the distribution of growth rates obtained pooling all Italian firms belonging to the same industry (2 digit NACE Rev. 2). This is a quick way to compare on and off park firms’ growth performance relatively to their (national) competitors, and it allows to notice that there is no substantial difference between the two groups.

Table 12 Share of firms in top and bottom quartiles of the distribution of growth rates
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Lamperti, F., Mavilia, R. & Castellini, S. The role of Science Parks: a puzzle of growth, innovation and R&D investments. J Technol Transf 42, 158–183 (2017). https://doi.org/10.1007/s10961-015-9455-2

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