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Public funding for innovation and the exit of firms

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Abstract

This paper focuses on the effect public subsidies for innovation have on the exit of firms. Utilizing Finnish firm level data I employ a kernel matching approach to eliminate the selection bias of public funding and estimate the counterfactual. As a robustness check a treatment model is estimated. Public funding for innovation exhibits a significant effect reducing the probability of exit. Distinguishing between exit by merger and exit by closure shows that public funding has a significant effect on the former. No significant effect on the latter can be found.

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Notes

  1. The R&D survey as well as the Community Innovation Survey is conducted by Statistics Finland. The database of Finnish innovations is built and maintained by the Technical Research Center of Finland – VTT.

  2. previously: National Technology Agency

  3. previously: Minstistry of Trade and Industry

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Acknowledgements

The analysis was started when the author was with the Technical Research Center of Finland (VTT). Funding by the Finnish Funding Agency for Technology and Innovation and the Finnish Ministry of Employment and the Economy is gratefully acknowledged. The analysis was conducted in the research laboratory of Statistics Finland. The author thanks Olavi Lehtoranta for his support and suggestions. Dirk Czarnitzki, two anonymous referees and the editors provided helpful comments and suggestions on earlier versions of the paper.

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  1. Management Center Innsbruck, Universitaetsstr. 15, 6020, Innsbruck, Austria

    Bernd Ebersberger

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Correspondence to Bernd Ebersberger.

Appendix

Appendix

1.1 A.1 Sensitivity with respect to the threshold

Tables 10 and 11 report the findings when the threshold of 50% in the heuristic classifying merger and closure is modified to 45% or 55%.

Table 8 Correlations
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Table 9 Probit regression of public funding
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Table 10 Exit by merger vs. exit by closure
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Table 11 Effect of public funding
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1.2 A.2 Treatment effect model

As the matching assumes that the underlying selection process only considers the observable characteristics of the firm, unobserved heterogeneity might bias the results of the analysis. The use of a bi-variate probit model will illustrate the robustness of the results obtained by the matching analysis. I estimate

$$ P\left( {y_1 =1,y_2 =1\vert \boldsymbol{x}} \right)=\Phi _2 \left( {\beta^\prime _1 \boldsymbol{x}+\gamma y_2 ,\beta^\prime _2 \boldsymbol{x},\rho } \right) $$
(8)

where y 1 is the dummy indicating exit (merger or closure), y 2 indicates public subsidies and x are the exogenous company charactersitics. Φ2 is the bivariate normal cumulative districution function, ρ is the covariance of the error terms of the two equations. Greene (2000) suggests that the bivariate probit model can be used to consistently estimate the effect of a binary treatment on a binary endogenous variable. Other than in the case of an OLS regression the potential edogeneity of the binary variable y 2 in the first equation can be ignored. The treatment effect on the treated is estimated based on the regressions in Tables 12, 13 and 14; the standard errors of the effects are estimated by the delta method.

Table 12 Bivariate probit—exit and public funding
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Table 13 Bivariate probit—exit by closure and public funding
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Table 14 Bivariate probit—exit by merger and public funding
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Ebersberger, B. Public funding for innovation and the exit of firms. J Evol Econ 21, 519–543 (2011). https://doi.org/10.1007/s00191-010-0186-0

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