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Do Direct Subsidies Stimulate New R&D Outputs in Firms? Evidence from the Czech Republic

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Abstract

This study examines output additionality effects of direct support to business R&D in the Czech Republic over 2004–2016. Using a large and rich firm-level dataset, we employ a non-parametric propensity score matching estimator to find out whether the subsidies stimulated new applications for formal intellectual property (IP) protection that would not have been made otherwise. The results indicate additionality effects for IP protection of R&D outputs at home but not abroad. Hence, the subsidies have fallen short of expectations for promoting new technology that is sufficiently novel to warrant international IP protection and thus could make a difference in foreign markets. The paper concludes with reflections on how subsidy programmes of this kind are justified, designed and evaluated.

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Notes

  1. For more discussion of the recent literature on competitiveness and growth, see, for instance, Fagerberg and Srholec (2017).

  2. It is interesting to note that even though similar arguments about promoting competitiveness are used also in the context of subsidies for small and medium sized enterprises, none of evaluation studies of such programmes that was surveyed by Dvouletý et al. (2020) used an outcome variable reflecting the international dimension, with the only exception of Beņkovskis et al. (2019) that looked at the effects on exports-to-turnover ratio.

  3. The only exceptions are the third call of IMPULS and the first call of TIP, both of which were announced in January and provided funding before the end of the same calendar year.

  4. The ISVaV data used in this study was valid on January 27, 2016 when a database snapshot was extracted from the original website: https://www.isvav.cz (Office of the Government of the Czech Republic, 2016). Since then, the database has been moved to a new domain: https://www.rvvi.cz (Office of the Government of the Czech Republic, 2017). Note that the ISVaV has unfortunately never provided data on unsuccessful applicants.

  5. In Amadeus database, missing data on the number of employees, location, legal form and industry was estimated using 1-year lag and 1-year lead.

  6. http://wwwinfo.mfcr.cz/ares/ares.html.en

  7. According to the merged PATSTAT-AMADEUS database, these sectors jointly account for about 89.2% of all applications for patents of invention filed by Czech enterprises with the selected legal forms during the period 2004–2013. These same sectors received the lion’s share of public R&D subsidies distributed through the IMPULS and TIP programmes. In particular, enterprises classified in C—Manufacturing and M—Professional, scientific and technical activities stand out, with a combined share of about 80% of the total amount of subsidies provided through the two programmes.

  8. Nevertheless, this potential problem is largely mitigated by using applications for IP protection within three years of the start of funding as the outcome variable.

  9. Since we narrow down the sample by sectors, but we do not know the sectoral classification of the supported firms not included in Amadeus, we cannot derive the exact percentage of firms in the targeted sample that is included in the analysis.

  10. Summary statistics of the variables is presented in Appendix Table 8.

  11. The diagnostics of the matching procedure, which turn out to be satisfactory, are presented in Appendix Tables 9, 10 and Figs. 1, 2.

  12. Results of the robustness checks are not reported for the sake of saving space but tables with the full results are available from the authors upon request.

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Acknowledgements

Earlier version of the paper was presented at the IDEA think-tank seminar on “Vedou státní dotace firemního výzkumu a vývoje k novým výsledkům”, Prague, June 15, 2017. We would like to thank Matěj Bajgar, Petr Horák and Daniel Münich and the seminar participants for their valuable comments and Jan Hanousek for facilitating access to firm-level micro data from Bureau Van Dijk’s Amadeus dataset. Any ambiguities, omissions or errors are the authors’ responsibility.

Funding

This study is financially supported by the Czech Science Foundation (GAČR) project no. 17-09265S on “Frontiers of empirical research on public financing of business R&D”.

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  1. CERGE-EI, a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences, Politických vězňů 7, Prague 1, 111 21, Czech Republic

    Oleg Sidorkin & Martin Srholec

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  1. Oleg Sidorkin
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Correspondence to Martin Srholec.

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Appendix

Appendix

Table 8 Descriptive statistics of the variables
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Table 9 Quality of propensity score matching: the number of observations on- and off-common support (NN 3 estimator)
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Table 10 The number of observations reused in matching based on the weight of matched controls (NN 3 estimator)
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Fig. 1
figure 1

Propensity score histograms for IMPULS (NN 3 estimator: common support, treated with scores > 0.1, untreated with scores > 0.1)

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Fig. 2
figure 2

Propensity score histograms for TIP (NN 3 estimator: common support, treated with scores > 0.1, untreated with scores > 0.1)

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Sidorkin, O., Srholec, M. Do Direct Subsidies Stimulate New R&D Outputs in Firms? Evidence from the Czech Republic. J Knowl Econ 13, 2203–2229 (2022). https://doi.org/10.1007/s13132-021-00812-y

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