Foreign Subsidiaries and Technology Sourcing in Spain

Abstract

Firms acquire external technological knowledge via different channels. In this paper, we compare the technology sourcing of foreign subsidiaries and domestic firms looking at domestic R&D outsourcing, international R&D outsourcing, domestic cooperation for innovation and international cooperation for innovation. We use data from the Spanish Technological Innovation Panel for the years 2005–2009 for 10,206 innovative firms operating in Spain. We apply a multivariate probit specification which allows for systematic correlations among the different choices. The results show that the different technology sourcing choices are interdependent and that foreign subsidiaries show a different pattern of external technology sourcing. Compared to affiliated domestic companies, foreign subsidiaries show a smaller propensity for external technology sourcing via R&D outsourcing from independent firms in the host country, for international R&D outsourcing and for international cooperation for innovation. In contrast, foreign subsidiaries show a greater propensity for domestic cooperation for innovation. However, foreign subsidiaries are not a homogenous group in this respect.

Keywords::

• Multinational enterprises
• foreign subsidiaries
• R&D outsourcing
• cooperation for innovation

Keywords::

• F23
• O32

Acknowledgements

We gratefully acknowledge the financial support of the Spanish Ministry of Science and Innovation (ECO2010-17485). We are grateful to three anonymous referees for their helpful comments and suggestions.

Notes

¹ We thank one anonymous referee for drawing our attention to this point.

² Refers to technology sourcing from sources external to the firm and its entrepreneurial group.

³ We use the freely available data-set that has been anonymized through microaggregation. For more details see López (2011).

⁴ The estimation is carried out using Stata's mvprobit code written by Cappellari and Jenkins (2003), which uses the Geweke–Hajivassiliou–Keane simulation method for maximum likelihood. In the field of cooperation for innovation, this approach was applied, for example, by Belderbos et al. (2004), Lenz-Cesar and Heshmati (2012), Schmidt (2008) and Carboni (2013).

⁵ Note that PITEC only provides the age of companies since the 2009 wave of the survey and thus only for those companies that responded to this wave.

⁶ Alternatively, we have also estimated the multivariate probits for separate waves of the survey. Results are qualitatively unchanged and available upon request.

⁷ The positive correlation could, however, also be driven by common omitted factors. The multivariate probit approach cannot distinguish these two possible sources for the observed correlation.

⁸ Ebersberger et al. (2011) study the effect of domestic multinationality in separate estimations and find a positive effect for both domestic and international cooperation for innovation.

⁹ We thank two anonymous referees for drawing our attention to this point.

¹⁰ We classified the manufacturing industries in which the plants operate into three groups according to the R&D intensity (average R&D/turnover) of the industry. In doing so we used the OECD classification, which establishes the following cut-off points for average R&D/turnover: 0.9; 3; and 5 per cent. The cut-off points define low, medium, and medium high and high technology industries.