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Measuring the economic impact of research joint ventures supported by the EU Framework Programme

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The objective of this paper is to analyse the effects of international R&D cooperation on firms’ economic performance. Our approach, based on a complete data set with information about Spanish participants in research joint ventures supported by the EU Framework Programme during the period 1995–2005, establishes a recursive model structure to capture the relationship between R&D cooperation, knowledge generation and economic results, which are measured by labour productivity. In the analysis we take into account that the participation in this specific type of cooperative projects implies a selection process that includes both the self-selection by participants to join the consortia and the selection of projects by the European Commission to award the public aid. Empirical analysis has confirmed that: (1) R&D cooperation has a positive impact on the technological capacity of firms, captured through intangible fixed assets and (2) the technological capacity of firms is positively related to their productivity.

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  1. Benfratello and Sembenelli (2002) consider a sample of firms participating during the period 1992–1994, indistinctly, and analyse their economic results for the period 1995–1996. Dekker and Kleinknecht (2008) use information for firms supported in FP4 and FP5, but they have no information about the concrete year of participation. Impact is measured considering sales of new products introduced in the market during the period 2002–2004 for the whole sample.

  2. See a detailed discussion of the different methods in the survey by Aerts et al. (2007).

  3. Notice that more than one firm can participate in the same proposal, and the same firm can participate in more than one proposal every year. To establish a clear correspondence between firms and projects, in our sample we have only included one project per firm and year.

  4. In fact, in their analysis of the effectiveness of the Eureka Program, Bayona-Sáez and García-Marco (2010) find that the completion of a Eureka project has a positive impact over firms performance measured as return over assets, although the effect does not manifest itself until a year after project completion.

  5. To guarantee the homogeneity of the sample, only Specific Targeted Research Projects (STREPs) and Integrated Projects are considered.

  6. Coverage of the data is basically restricted to firms that have at least 10 employees (annual average), but we have also included 615 micro-companies (0.5% of the CCD, chosen again by means of a random sampling scheme), given that 219 applicants of cooperative FP projects belong to this category.

  7. Proposals are evaluated by independent experts according to some common criteria. However, such information is absent from our database.

  8. The Heckman procedure for the binary response variable in STATA does not take into account the panel structure of the data and the information is treated as a pool. However, in Barajas and Huergo (2010), the decision to apply has been estimated as a random-effects probit model taking into account the panel structure of the data and the results are basically the same.

  9. Find the exact definitions of the variables in Appendix 1.

  10. The FP6 introduced new instruments, such as the Integrated Projects and the Networks of Excellence, giving priority to projects with more ambitious goals, longer development terms and bigger budgets.

  11. This evidence is consistent with Busom and Fernández-Ribas (2007), who find that the key characteristic of participants in European programs is the extent to which the firm operates in foreign markets, as measured by export intensity.

  12. The results do not differ when the firm number of employees is included instead of the set of size dummies. The same happens in Tables 4 and 5.

  13. Barajas and Huergo (2010) present complementary estimations for two sub-samples: SME and large firms. They found a non-linear effect of size which is negative for the SME and positive for large firms.

  14. We also include some other industry dummies with statistically significant effects. Specifically, Education, Clothing apparel and footwear and Other business activities, which include architectural and engineering activities and related technical consultancy, should be noted. An opposite case is the Hotels and restaurants industry, with a lower probability of participating in technological projects. In reference to the Energy sector, both FP4 and FP5 had specific programmes for the development of sustainable energies, which increased the occasions for firms to present proposals. The results are available from the authors upon request.

  15. As control variables we also consider a set of geographical dummies, given that the more technological Spanish firms tend to locate in specific regions. In particular firms located in the Basque Country, Catalonia, Madrid and Valencia show higher probabilities of submitting an application, which is consistent with the major concentration of technological firms in these regions.

  16. Most of the total budget of the FPs is allocated to information and communication technologies. Therefore, it seems that the EU gives priority to these technological areas. Our result that only firms which carry out FP programmes in Transport show a greater probability of receiving aid can be a consequence of the criteria followed to eliminate the firms which have more than one project. Most of the dropped observations are projects belonging to ICT, Transport and Aeronautical technologies areas.

  17. Most previous empirical evidence approaches technological inputs by R&D expenditures and new knowledge by product and process innovations, or sales generated by new products. However, this information is not available in our database.

  18. Spanish accounting rules allow for the capitalisation of R&D expenditures under certain conditions (mainly when there are reasonable expectations of marketable results).

  19. When we try the larger period (t + 6), the number of observations is reduced significantly and it is more difficult to capture robust effects. However, knowledge generated in previous periods is still a positive determinant of labour productivity in (t + 6).

  20. See the results of the estimates for Eqs. 3 and 4 in Tables 7 and 8 of Appendix 2.

  21. See Table 9 of Appendix 2.


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The authors thank Isabel Busom, Luis Corchón, Pierre Mohnen and the audiences at the ASIGO Conference (Nuremberg), the 36th Conference of the EARIE (Ljubljana), the CONCORD 2010 (Sevilla), the seminar at the Departament d’Economia Aplicada of the Universitat Autònoma de Barcelona and the seminar of the CSIC Institute of Public Goods and Policies for their helpful comments. All errors are ours. This research has been partially funded by the CICYT projects SEJ2007-65520/ECON and ECO2010-18947/ECON. Elena Huergo and Lourdes Moreno also acknowledge financial support from CDTI to develop this research.

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Correspondence to Lourdes Moreno.


Appendix 1

See Table 6.

Table 6 Definition of variables

Appendix 2

2.1 Complementary estimates

See Tables 7, 8 and 9.

Table 7 Growth of intangible fixed assets per employee (t + 5)
Table 8 Labour productivity growth (t + 5)
Table 9 ETBIDA (t + 5) and sales (t + 5)

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Barajas, A., Huergo, E. & Moreno, L. Measuring the economic impact of research joint ventures supported by the EU Framework Programme. J Technol Transf 37, 917–942 (2012).

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