R&D funding and private R&D: empirical evidence on the impact of the leading-edge cluster competition

Abstract

This paper analyzes the effects of the governmental financing instrument Spitzencluster-Wettbewerb (Leading-Edge Cluster Competition, LECC) on R&D expenditure of firms in Germany. The LECC promotes cooperative research among business firms and research institutions under the umbrella of a common strategy, which is pursued by regional cluster organizations. We measure the effect of LECC funding on private R&D spending as well as the effects of the policy instrument on the composition of R&D (internal vs. external). Our analysis is mainly based on data from the R&D survey for Germany. We combine propensity score matching (to identify statistical twins) with a difference-in-differences estimator in order to measure the causal effects of the LECC. These results are complemented with the findings from expert interviews. Our results show that the LECC significantly increases R&D expenditures in comparison to non-funded firms. On average, we did not find evidence of crowding out. At the same time, we identified a greater leverage effect of the LECC for small and medium-sized firms. A comparison with companies that have been funded in other R&D-programs shows that the LECC leads to a greater increase in R&D expenditure in small and medium-sized enterprises (SMEs). The expert interviews in general confirm these results and indicate that there are different patterns at firm level depending on firm size, strategy, and sector. In addition, they reveal that the effect of co-funding rules for R&D expenditure appears to be stronger for SMEs.

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Notes

  1. 1.

    According to the program description on the official LECC website [http://www.bmbf.de/en/20741.php, download 03.06.2015].

  2. 2.

    For further information on the program see Rothgang et al. 2014.

  3. 3.

    For a total account of all program targets, see Rothgang et al. 2014: 17.

  4. 4.

    This provision is part of the ministry guideline documents of all three competition waves, which are no longer available on the internet.

  5. 5.

    See e.g. https://www.bmbf.de/de/spitzencluster-foerderung-zahlt-sich-aus-studie-belegt-leistungsfaehigkeit-der-15-1506.html, accessed on February 15, 2017.

  6. 6.

    The hybrid matching procedure results in a better balance between both groups than (simple) propensity score matching. The results can be obtained from the authors on request.

  7. 7.

    The matching procedure is carried out using software package psmatch2 in STATA 12 (see Leuven and Sianesi 2003).

  8. 8.

    The R&D statistics of the SV Wissenschaftsstatistik are part of the official reporting on research, development and innovation by the Federal Government to the EU and the OECD. The underlying definitions of R&D indicators are based on internationally standardized rules that have been set in the "General Guidelines for Surveys on Research and Experimental Development" (Frascati Manual) of the OECD

  9. 9.

    Every project funded by the German Federal Ministry for Education  and Research (Bundesministerium für Bildung und Forschung) has a identifying funding number (in German: Foerderkennzeichen).

  10. 10.

    Results are available from the authors upon request.

  11. 11.

    For the sample ‘Firms of the second wave only (2009–2011)’ we are able to take the growth rate of R&D expenditures between 2007 and 2009 in consideration as well.

  12. 12.

    We cannot exclude the possibility that the extent of previous funding differs between funded and non-funded firms. The difference in the outcome variable is biased upwards when the public funding/total R&D expenditure ratio is lower for funded firms than for non-funded firms with funding at t−1.

  13. 13.

    Since the means are potentially highly biased in very small samples, we report median values only.

  14. 14.

    For comparison: Aschhoff et al. (2012, p. 15) detect a very similar share of 20 per cent for funded SMEs in the BMBF program ‘KMU innovativ’.

  15. 15.

    The calculation in detail is as follows: total R&D expenditurest = private R&Dt + total R&Dt × share of public fundingt leads to the formula: total R&Dt = private R&D/(1 - share of public fundingt) and then to expected total R&D of 144.7 (= 118.2/(1-0.183)) compared to the index value of 100 in the period before funding.

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Correspondence to Michael Rothgang.

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Engel, D., Eckl, V. & Rothgang, M. R&D funding and private R&D: empirical evidence on the impact of the leading-edge cluster competition. J Technol Transf 44, 1720–1743 (2019). https://doi.org/10.1007/s10961-017-9609-5

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Keywords

  • R&D
  • Public subsidies
  • Collaboration
  • Policy evaluation

JEL Classification

  • C14
  • C25
  • H50
  • O38