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Innovation spillovers and firm performance: micro evidence from Spain (2004–2009)

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Abstract

This article analyses the impact that R&D expenditures and intra- and inter-industry externalities have on the performance of Spanish firms. Despite the extensive literature studying the relationship between innovation and productivity, there are far fewer studies in this particular area examining the importance of sectoral externalities, especially focused on Spain. One novelty of this study, conducted for the industrial and service sectors, is that we also consider the technology level of the sector in which the firm operates and firm size. The database used is the Technological Innovation Panel. It comprises 9985 firms over the period 2004–2009 and has been used infrequently for studies of this type. The Olley and Pakes (Econometrica 64:1263-1297, 1996) estimator is adopted in order to account for both simultaneity and selection biases providing consistent estimates. The results suggest that, unlike previous studies, R&D expenditures do not have a direct impact on firm performance. By contrast, spillovers do. In particular, intra-industry externalities present a positive and significant effect in low-tech and large firms. Inter-industry externalities, however, present an ambiguous effect and there appears to be no specific pattern of behaviour associated with technology level or firm size.

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Notes

  1. It should be mentioned that Goya et al. (2012) carry out a similar analysis; however it is a much basic work using cross-section data for 2010. In the present paper we use panel data and we are able to capture changes over time as well as tackle several problems that appear when a productivity analysis is undertaken. Unobserved heterogeneity or simultaneity issues need to be taking into account since they might be affecting the relationship between innovation and productivity. The estimation method used here (Olley and Pakes 1996) account for these problems providing consistent results.

  2. By technology level we mean the level of technology of the sector in which the firm operates. As it will be seen in Sect. 4.1 firms can be classified as high-tech manufacturing industries (HTMI), low-tech manufacturing industries (LTMI), knowledge-intensive services (KIS) and non-knowledge intensive services (NKIS). This is an interesting factor to include in the analysis as technological opportunities and appropriability conditions are different between sectors which can lead to differences in the influence of R&D. According to previous studies (see next section), investments in R&D carried out by firms operating in “more advanced” sectors (HTMI and KIS) are more fruitful in increasing firm´s productivity than investments undertake by firms operating in “less advanced” sectors (LTMI and NKIS).

  3. The ESEE is a firm-level survey of Spanish manufacturing which collects annual information since 1990.

  4. It should be pointed out that only a few microeconomic studies, especially for the case of Spain, have incorporated human capital and innovation as factors in the production function.

  5. See for instance, Black and Lynch (1996) and Haltiwanger et al. (1999) for the United States, Turcotte and Rennison (2004) for Canada, Arvanitis and Loukis (2009) for Greece and Switzerland, Yang et al. (2010) for China and Lee (2011) for Malaysia.

  6. Last year available when this paper was written.

  7. We do not use data for 2003 because of its severe limitations. PITEC began with just two samples in 2003 (a sample of firms with 200 or more employees and a sample of firms with internal R&D expenditures). In 2004, it overcame this limitation by including a sample of firms with fewer than 200 employees with external R&D expenditures but which carried out no internal R&D and a sample of firms with fewer than 200 employees with no innovation expenditures.

  8. This filtering process meant eliminating those observations that included any kind of ‘incident’ (i.e., confidentiality problems, takeovers, mergers, etc.) and those containing obvious anomalies (such as null sales).

  9. The population area is as defined by the Spanish Innovation Survey on which PITEC is based.

  10. Unfortunately, PITEC does not contain information about intermediate consumptions, prices or other information necessary to compute total factor productivity (or multi-factor productivity). Thereby, this kind of analysis is not possible here. The only measure available regarding output is firm sales (this is something common when Innovation Surveys are used, for instance Community Innovation Survey).

  11. We are aware that the concept of innovation is very wide including not only R&D expenditures, but also, the acquisition of machinery, equipment and hardware/software, training staff directly involved in developing the innovation, introduction innovation in the market, design, etc. However, in line with previous studies, we approximate innovation with R&D expenditures (both internal and external).

  12. Although industry dummies would capture technological opportunities as well as specificities of the sector, we cannot include them since it would give rise to perfect multicollinearity with the inter-industry externalities.

  13. See Hall and Mairesse (1995), Bönte (2003) and Ortega-Argilés et al. (2011) to name just a few.

  14. The results are almost identical if a fix depreciation rate is used (6 % for physical capital and 15 % for innovation).

  15. Note, however, that the choice of g does not modify the results greatly. As Hall and Mairesse (1995) report in footnote 9: “In any case, the precise choice of growth rate affects only the initial stock, and declines in importance as time passes…”.

  16. Even though the evidence suggests that simultaneity bias is more important than selection bias, the estimator used here controls for both.

  17. As shown in the literature, simultaneity problems can be addressed by using instrumental variables or fixed effect estimators. However, given that the panel is short and instrumental variables use lagged values as instruments, their suitability is reduced (facing a problem of weak instruments). The fixed effects estimator, on the other hand, relies on the assumption that productivity shocks are constant over time, which is an excessively strong premise from our point of view, especially bearing in mind the economic situation in the period under analysis.

  18. Another option might be the Levinsohn and Petrin (2003) estimator, but intermediate inputs are needed in this case (and this information is not provided by PITEC). Thus, we strongly believe that the best option available is the OP estimator.

  19. For a detailed explanation of the equations and estimation strategy see Olley and Pakes (1996).

  20. Estimates are obtained using the “opreg” command in Stata (Yasar et al., 2008).

  21. PITEC includes this information from 2009.

  22. The authors modify the OP framework by introducing the decision to engage in international trade.

  23. The test was applied both to the whole sample and on a year-by-year basis.

  24. The nonparametric Kruskal–Wallis test is a safer alternative than parametric tests in which there are concerns about the normality assumptions or suspicions of outlier problems.

  25. It should be borne in mind that the aim here is not to compare the two approaches, but rather to provide a plausible explanation for the results obtained.

  26. We would like to thank an anonymous referee for pointing this out.

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Acknowledgments

This research has received funding from the European Union’s Seventh Framework Programme FP7-SSH-2010-2.2-1 (2011–2014), under grant agreement n° 266834. The authors are grateful for the funding obtained from the Ministry of Education and Science for the project entitled “Determinants for the spread of innovation and their effects on productivity”, ECO2009-12678/ECON, 2010–2012. Esther Goya is grateful for the support received from the CUR of the DIUE of the Generalitat de Catalunya and from the European Social Fund. Esther Vayá is grateful for the funding obtained from the Ministry of Education and Science for the project entitled “Regional economic growth and inequality in Spain”, ECO2010-16006/ECON, 2011–2013. Jordi Suriñach is grateful for the funding obtained from the Ministry of Education and Science for the project ECO2013-41022-R. The authors are grateful for helpful comments received at the AQR Lunch Seminar celebrated on the 9th of October, 2012 at the University of Barcelona.

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Authors and Affiliations

  1. AQR-IREA Research Group, Faculty of Economics and Business, University of Barcelona, Av. Diagonal 690, Tower IV, Barcelona, Spain

    Esther Goya, Esther Vayá & Jordi Suriñach

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  1. Esther Goya
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Correspondence to Esther Goya.

Appendices

Appendix 1: Treatment of extreme values

The table below reports the number of firms with more than double the volume of sales by technological level. These observations have been replaced by the double of sales (see Table 6, 7).

Table 6 Outliers by technological level
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Table 7 Outliers by firm size
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Appendix 2: Correspondence between branches of business activity

See Table 8.

Table 8 Correspondence between PITEC and NACE-Rev. 1.1classification
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Appendix 3: Distribution of firms according sub-samples

See Table 9.

Table 9 Sample distribution
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Appendix 4: Intermediate results

See Table 10 and 11.

Table 10 Results from the partially linear model Eq. (13)
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Table 11 Results from Probit model Eq. (14)
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Goya, E., Vayá, E. & Suriñach, J. Innovation spillovers and firm performance: micro evidence from Spain (2004–2009). J Prod Anal 45, 1–22 (2016). https://doi.org/10.1007/s11123-015-0455-x

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