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Economic Footprint of a Large French Research and Technology Organisation in Europe: Deciphering a Simplified Model and Appraising the Results

Journal of the Knowledge Economy volume 13, pages 44–69 (2022)Cite this article

Abstract

Europe has seen the importance of Research and Technology Organisations (RTOs) grow in recent years. This is hardly surprising given that their main mission is to harness science and technology to foster innovation that can improve the quality of life and boost economic competitiveness. In the current economic context, it is key to demonstrate the positive impact of their activities on the economy and society, i.e. spin-offs, considering that they receive public subsidies. Accordingly, the European Association of Research and Technology Organisations (EARTO) published a report in March 2018 that details the economic footprint of 9 of its members, which includes the French Alternative Energies and Atomic Energy Commission (CEA). This French organisation, whose budget represents 60% of the total budget from all 9 RTOs, was the subject of a separate calculation in the framework of the research carried out for the paper: data was provided by the CEA and simulation was performed on the basis of the model provided by IDEA Consult. This article deals with the provision of empirical results about the CEA and makes a comparison with the results obtained for the 9 RTOs using the same methodology. This case study highlights information that can be used to provide a more accurate assessment of the CEA’s economic impact based on its specificities compared with its European counterparts. In addition to estimating the economic spin-offs of technology transfers, it is equally important to take into account value creation associated with the procurement of high technology—the impact of “big science”—which is substantial in the CEA’s defence sector and undervalued in the economic model used by IDEA Consult. Thanks to a literature review summarising the advantages and limitations of the different impact assessment approaches and thanks to interviews at CEA management level—that helped us to better understand the impact-generating mechanisms—the paper opens new avenues of research to improve the methodology for measuring the impact of research organisations that are very diverse in their duties. The new methodology proposed takes into account the spillovers of their activities, as well as offering useful approaches for governments and the European Commission. More specifically, it proposes to apply the same methodology—in the opposite direction—to measure the spin-offs of the contract research activities (with a multiplier as in the methodology used by IDEA Consult) and to estimate the science market characterised by very specific calls for tender that generate innovation. Using the same model, we therefore propose to couple an estimate of the footprint of jobs and standard purchases, with an estimate of the spin-offs linked to both technology transfers (RTO to industry) and to high-tech purchases (industry to RTO).

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Notes

  1. Including solar photovoltaic, wind power, electric vehicles, Li-Ion batteries, carbon capture and storage, biofuels

  2. High council for the evaluation of research and higher education

  3. In March 2002, Barcelona’s European Council agreed to increase investment in R&D to 3% of the GDP by 2010.

  4. Austin and Macauley (2000), p. 5

  5. Such a test was performed in a study from the UK’s Economic and Social Data Service (ESDS) by Beagrie and Houghton (2012), which is relatively rare in the literature analysed.

  6. For every European Currency Unit (ECU) invested by the European tax payer, almost 3 ECUs of measurable economic wealth is generated.

  7. See the European project (2011–2013) called EvaRIO (Evaluation of Research Infrastructures in Open innovation and research systems)

  8. For every Swiss franc spent by the CERN in the high-tech field between 1973 and 1987, 3 Swiss francs in new business with contractors was generated.

  9. Multiplier effects associated with the expense of purchasing supercomputers: the 1 million euros spent on a supercomputer manufactured in France generated a total of €2.49 million in production, €0.96 million in added value, and 12 jobs. The multiplier effects of maintenance expenses were €2.82 million, €1.44 million and 7 jobs, respectively.

  10. The 1 million euros in subsidies paid to the Dutch research organisation, TNO, generated €1.88 million in production, €1.15 million in added value (GDP) and 13.3 jobs.

  11. The employment multiplier for STMicroélectronique was around 4 in 2012.

  12. EARTO report (IDEA) is published in 2018 (Bilsen et al. 2018) and in 2015 (Bilsen et al. 2015) for 9 RTOs and for the CEA (Bilsen and Van Hoed 2018), BIGGAR Economics (2015 and 2017), CEA Tech (2017).

  13. Two additional jobs for each job created in the public research organisation.

  14. This means that for every £1 million of public money spent, between £2 million and £3 million are generated in the economy through the purchase of goods and services and wage spending.

  15. Syndex (2012) & INSEE analyses (2014)

  16. French National Research Agency

  17. French Environment and Energy Management Agency

  18. Commissariat-General for Investment

  19. A multiplier equal to 1.98 was applied. Averaged over Europe, this multiplier represents the total technology intensity of a country compared with its R&D effort (Knell 2008; Hauknes and Knell 2009): \( \sum \frac{Marketed\ products\ and\ equipments\ incorporating\ R\&D}{Countr{y}^{\prime }s\ total\ R\&D\ } \)

  20. This type of modified footprint method has also been used to estimate the economic impact of CEA Tech (2017).

  21. The “pôles de compétitivité” are the main instruments of the French cluster policy.

  22. HPC has become a key technology for research, industry and services. It most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering or business.

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Acknowledgements

The author would like to warmly thank Muriel Attané, Secretary General of EARTO and her team, as well as Miriam Van Hoed, Expert at IDEA and her team, without whom this study and the discussions that followed could not have taken place. The author, however, remains responsible for the interpretations of the results of the footprint analysis model used. The author also wishes to thank all those at the CEA who devoted their precious time to provide the data needed for the footprint analysis.

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    Nathalie Taverdet-Popiolek

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Taverdet-Popiolek, N. Economic Footprint of a Large French Research and Technology Organisation in Europe: Deciphering a Simplified Model and Appraising the Results. J Knowl Econ 13, 44–69 (2022). https://doi.org/10.1007/s13132-020-00709-2

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Keywords

  • Economic impact of research
  • Economic spin-offs of R&D
  • Economic footprint
  • Technology transfer
  • Co-creation of knowledge
  • Procurement of high technology
  • Case study