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The impact of science and technology parks on firms’ product innovation: empirical evidence from Spain

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Abstract

Science and Technology Parks (STP) have attracted considerable attention and public funds in recent years. However, the conclusions on their effectiveness remain mixed. This work evaluates the impact of STP on firm product innovation in the Spanish context, as an example of a less developed innovation system in which regional and national governments are prioritizing STP initiatives. This work draws on a large sample of firms provided by the Spanish Survey on Technological Innovation that is conducted annually by the National Statistical Institute. We explore alternative econometric methods to obtain average treatment effects for firms located in 22 Spanish STPs. Our results show that Spanish STPs have a strong and positive impact on the probability and amount of product innovation achieved by STP located firms. These results hold when the endogeneity of STP location is taken into account.

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Notes

  1. UK: Monck et al. (1988); Westhead and Storey (1994); Westhead (1997); Siegel et al. (2003a, b). USA: Link and Scott (2003, 2004, 2006, 2007). Sweden: Löfsten and Lindelöf (2001, 2002, 2003); Lindelöf and Löfsten (2003, 2004); Ferguson and Olofsson (2004); Dettwiler et al. (2006).

  2. Data are drawn from the 2007 Spanish Technological Innovation Survey, managed by the National Statistical Institute (INE).

  3. Among the studies reviewed, only Siegel et al. (2003a), Fukugawa (2006) and Yang et al. (2009) tried to take account of endogeneity.

  4. The first financial support by the central government amounted to 300 million spreading across 2000–2003 (Infyde 2008) and increased to approximately 400 million for the 2004–2007 period (Spanish Ministry of Science and Innovation).

  5. For a revision of the literature, see Imbens and Wooldridge (2009) or Guo and Fraser (2010).

  6. This approach is commonly used in innovation studies. See for example, Hoisl (2007) or Cardamone (2012).

  7. The specific characteristics of this sample are available on the INE webpage: http://www.ine.es/ioe/ioeFicha.jsp?cod=30061

  8. Its limitations include that larger firms will show very high turnovers based on previously consolidated products, resulting in a lower indicator despite high monetary income from new products; it is very sensitive to product life cycle; and the market in which the company operates is used as the reference, but may not be the same for two competing companies, e.g. if one exports and the other does not (Kleinknecht et al. 2002; Frenz and Ietto-Gillies 2009).

  9. Our approach is similar to that of Gelabert et al. (2009). They also use CIS Data and analyze the effect of public subsidies on firms’ results. They build a measure of ‘Subsidies available to firms at the regional level’ and use it as an instrument.

  10. E.g., Johansson and Hans (2008) find that the propensity to be innovative differs among regions, but that among innovative firms, the intensity of innovation is not influenced by location. Sternberg and Arndt (2001) in a study of SMEs find that firm-specific determinants of innovation are more important than either region-specific or external factors.

  11. We conducted estimations using other IV related to region: regional distribution of physical space (in sq. m.) dedicated to parks. The results are very similar to those obtained using Z and, therefore, are not included here; they are available upon request from the authors.

  12. There are two censor points in the dependent variable tlnewmar: c 1 = 0 in 34,659 observations (87.25 % of cases) and c 2 = 6.90 in 604 observations (1.5 %).

  13. Depending on the specific configuration of the innovation surveys, some authors, using the same dependent variable, consider there to be a problem of missing data since the only firms able to report sales due to new products are those that have obtained new products, and therefore choose generalized Tobit or selection models (see e.g., Mohnen and Dagenais 2000; Mairesse and Mohnen 2001, 2005; Raymond et al. 2006; Eom and Lee 2010.) In the Spanish case, we consider that there is no such selection problem, since all the surveyed firms are required to respond to the questions related to innovation inputs and innovation outputs. Thus, firms with no new products have zero sales from new products. We follow Negassi (2004) and Laursen and Salter (2006) and the recommendation in Mairesse and Mohnen (2010).

  14. We ran the regressions including these variables but the results did not change, which is logical since their coefficients are very close to zero. Note that the different roles of the obstacles for innovators and non innovators is a highly controversial issue (D’Este et al. 2012).

  15. Because we consider only innovative companies, the possibility of observing zeros in this indicator is eliminated.

  16. Results from first step are shown in Appendix 2. The instrument clearly satisfies the inclusion restriction.

  17. When instrumental variable probit estimators of this type are used, coefficients are consistent but standard errors are not (Adkins 2012). This is the reason why the coefficient in the first part of the two part model is not found to be significant. When OLS is used, this problem is overcome and the coefficient becomes significant again.

  18. Although the coefficients point to a non-linear influence, the negative effect of size holds only for firms with less than €1,040 worth of sales in the double censored Tobit and less than €895 worth of sales in the probit model. Conversely, in the second part of the two part model, the effect is negative after €503 worth of sales.

  19. This result is also in line with previous literature. When only innovators are analyzed and controls for innovation effort, size and innovation strategies are included, no significant difference is observed across sectors (Kirner et al. 2009; Tsai 2009; Hung and Wang 2012).

  20. There are two complementary explanations for the negative effect on cooperation. On the one hand, the effect of cooperation on innovation performance is still a matter of debate, as some studies have found negative effects (for example, Tsai 2009) and most studies have not found any significant effect (for a review of previous studies using CIS surveys, see Barge-Gil 2013). In addition, we also include in the regression the importance of different information sources, which show a positive effect on performance. The negative effect of cooperation is driven by the inclusion of these other variables (no significant effect is found when excluded). The reason being that those firms succeeding in cooperation answer that importance of external sources is high, while those failing in cooperation answer that importance of external sources is low, so that this variable is capturing most of the positive effect, of cooperation.

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Acknowledgments

The authors want to thank the Spanish Institute of Statistics for allowing access to the data and Annamaría Conti and Alberto López for comments on previous versions of the paper. A version of this paper was presented at DRUID 2010 and Encuentro de Economía Aplicada 2010. The usual disclaimers apply. We acknowledge funding from project “Evaluación del Impacto de los Parques Científicos y Tecnológicos Españoles”, funded by Spanish Department of Science and Innovation.

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Authors and Affiliations

  1. Laboratory of Analysis and Evaluation of Technical Change, Carlos III University of Madrid, Calle Madrid 126, 28903, Getafe Madrid, Spain

    Ángela Rocío Vásquez-Urriago

  2. Department of Economic Analysis II (Quantitative Economics), Complutense University of Madrid, Madrid, Spain

    Andrés Barge-Gil

  3. Department of Economics, Laboratory of Analysis and Evaluation of Technical Change, Carlos III University of Madrid, Madrid, Spain

    Aurelia Modrego Rico

  4. Department of Business Administration, Carlos III University of Madrid, Madrid, Spain

    Evita Paraskevopoulou

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  1. Ángela Rocío Vásquez-Urriago
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  2. Andrés Barge-Gil
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Correspondence to Ángela Rocío Vásquez-Urriago.

Appendices

Appendix 1: Studies using indicators related to innovative product sales

Table 8 Studies using indicators related to innovative product sales
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Appendix 2: First step of IV regression with propensity score

Table 9 First step of IV regression with propensity score
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Vásquez-Urriago, Á.R., Barge-Gil, A., Rico, A.M. et al. The impact of science and technology parks on firms’ product innovation: empirical evidence from Spain. J Evol Econ 24, 835–873 (2014). https://doi.org/10.1007/s00191-013-0337-1

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