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Evaluating the indirect effects of cluster-based innovation policies: the case of the Technological Research Institutes in France

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Abstract

When it comes to evaluating the causal effect of public policies on corporate performance, most studies tend to focus exclusively on targeted firms, as if these firms have no relationship to the rest of the economy. Yet, public policies are highly likely to influence non-targeted firms indirectly due to the relationships they have with targeted firms. This paper aims to fill this gap by evaluating the indirect causal effect of a new French cluster-innovation policy on the financial and employment outcomes of non-targeted companies. To do so, it focuses on French Technological Research Institutes, which are science-industry collaborations based on technology platforms that bring together SMEs, large companies, universities, and public research bodies with the goal of accelerating the transfer of knowledge towards firms and generating spillovers (indirect effects) inside and outside the scheme. Based on the literature on spillover effects and agglomeration economies, it can be assumed that industry-specific spillovers tend to be spatially concentrated. By comparing a non-targeted firm located in the NUTS-3 regions within which the policy was implemented (referred to as "treated regions"), to a non-targeted firm outside of these "treated regions", using a difference-in-differences method with fixed effects applied to panel data (2008–2016) combined with a double matching at the NUTS-3 region and firm level, we find that non-targeted firms located in the "treated regions" significantly improve their financial performance (turnover, financial autonomy) compared to control firms located in the NUTS-3 control regions. The dynamics of employment outcomes are ambiguous. A negative significant effect is observed on the proportion of managers at the beginning of the policy and a positive significant effect is noted later, at the end of the period of observation. An analysis of the dynamics of the effects indicates that performance does not improve immediately after the policy, but later in time.

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Notes

  1. Stable Unit Treatment Value Assumption.

  2. Cohen and Levinthal (1989) introduced the notion of absorptive capacity as the firm's ability to value, assimilate, and apply new knowledge for improving organizational learning.

  3. Cited by Anselin et al. (1997).

  4. In English, “Investment in the future programme”.

  5. The Commission for Atomic Energy and Alternative Energies is a French public scientific research organisation in the fields of energy, defence, information technologies, material sciences and life and health sciences, based in ten sites across France. Historically known as the Atomic Energy Commission (CEA), it changed its name in 2010 and expanded its scope to alternative energies.

  6. The Laboratory of Electronics and Information Technology (LETI) is one of the world’s leading applied research centres for microelectronics and nanotechnology. Based in the scientific polygon in Grenoble, France, it is one of the 34 members of the network of Carnot institutes to promote innovation and economic dynamism in collaboration with industry.

  7. European Observatory of Localised Data on Innovation.

  8. Conditional Independence Assumption.

  9. Stable Unit Treatment Value Assumption.

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Acknowledgements

I would like to thank the JTT associate editor for his valuable advice, for providing me with several bibliographic references that enriched this article, and the three anonymous reviewers for their valuable comments. I would also like to thank Bruno Ragué, Deputy Director of IRT Nanoelec, for giving me access to internal data. I will conclude by thanking my two thesis supervisors Corinne Autant-Bernard and Nadine Massard for supervising my thesis work.

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  1. GREThA UMR 5113, Plateforme VIA INNO, University of Bordeaux, 6 Avenue Léon Duguit, 33600, Bordeaux, France

    Ruben Fotso

  2. GATE UMR 5824, Univ Lyon, UJM Saint-Etienne, 42023, Saint-Étienne, France

    Ruben Fotso

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Appendices

Appendix 1

See Figs. 4 and 5

Fig. 4
figure 4

Common trends hypothesis: evolution of outcomes relative to the two groups (treated vs. controls) before the policy (2008–2011)

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Fig. 5
figure 5

Common trends hypothesis: evolution of outcomes relative to the two groups (treated vs. controls) before the policy (2008–2011)

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Appendix 2

See Tables 8, 9, 10, 11, 12, 13 and 14

Table 8 Testing the common trends hypothesis
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Table 9 The permanent indirect effect of the TRI on SME Performance—summary of the results of the indirect effect evaluation (with another control group)
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Table 10 Indirect annual effect of the Nanoelec TRI (with another control group)
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Table 11 Permanent effects on financial variables: difference-in-difference with fixed effects estimator
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Table 12 Permanent effects on employment variables: difference-in-difference with fixed effects estimator
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Table 13 Annual effects: difference-in-difference with fixed effects estimator
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Table 14 Annual effects: difference-in-difference with fixed effects estimator
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Fotso, R. Evaluating the indirect effects of cluster-based innovation policies: the case of the Technological Research Institutes in France. J Technol Transf 47, 1070–1114 (2022). https://doi.org/10.1007/s10961-021-09865-2

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