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R&D, firm growth and the role of innovation persistence: an analysis of Finnish SMEs and large firms

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Abstract

R&D investment and growth in SMEs and large firms relate in a complex way. This paper analyses what role persistence of innovation output plays in shaping that relationship. We apply a vector auto-regression model to Finnish firm-level data and summarize the lead–lag relationship and complex co-movements of R&D growth and firm growth series. We found only continuous product and process innovators to have positive associations between R&D growth and sales growth. Also the associations between sales growth and subsequent R&D growth were stronger for continuous innovators than for occasional innovators, but only for product innovators. In the case of process innovators it is the occasional innovators that exhibit a stronger association between sales growth and subsequent R&D growth. In addition, our results vary between large and small firms. We express the need for further research on innovation dynamics and growth of SMEs analysing the interactions between different innovation activities.

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Notes

  1. Following Raymond et al. (2010) we restrict the sample to firms that have at least two consecutive observations in the bi-annual CIS survey as to be able to define each firm in our sample as either being an occasional or continuous innovator.

  2. Insignificant numbers refer to employment smaller than 1 and total R&D expenditures and sales smaller than 1,000 Euro.

  3. R&D intensity is defined as the sum of internal and external R&D expenditures as a share of total sales.

  4. For the alternative definitions of innovation persistence we considered Malerba et al. (1997), Cefis and Ciccarelli (2005), Cefis and Ghita (2008), Clausen et al. (2012) and Demirel and Mazzucato (2012).

  5. As Coad (2010) points out although the “Nickel-bias” is often observed to be small (Nickell 1981) it could indeed be dealt with by using IV techniques such as GMM (Blundell and Bond 1998). The problem here is that it is difficult to find suitable instruments for growth rates because they are in nature random and lack persistence (Geroski 2000). IV estimates of a panel VAR with weak instruments thus leads to imprecise estimates. Binder et al. (2005) present a panel VAR model which can include firm-specific effects but that does not require the use of Instrument Variables. The drawback with their model for this particular application is that it assumes normally distributed errors whereas the distribution of firm growth rates are approximately Laplace distributed.

  6. Due to the exploratory nature of our reduced VAR-model this should not be considered as a draw-back.

  7. Taking into account the confidence intervals (grey zone) in Fig. 1 and Fig. 2 our asymmetry results have to be interpreted with care.

  8. With “asymmetry result” we here refer to the finding that declining firms do not decrease R&D expenditure with the same ease that growing firms can increase R&D expenditure.

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Acknowledgments

This paper was initiated in the “FINNENTRE” project financed by Tekes and VTT. We thank discussants at the “firm growth and innovation” workshop in Tarragona on 29.6.2012, Alex Coad, Ari Hyytinen, Christopher Palmberg, and two anonymous referees for valuable comments and Statistics Finland for data access.

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Authors and Affiliations

  1. School of Business and Economics, University of Jyväskylä, Jyväskylä, Finland

    Matthias Deschryvere

  2. VTT Technical Research Centre of Finland, Espoo, Finland

    Matthias Deschryvere

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  1. Matthias Deschryvere
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Correspondence to Matthias Deschryvere.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10 and 11, Fig. 2.

Table 6 Matrix of contemporaneous standard and Spearman correlations for the indicators of firm growth, pooling the years together
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Table 7 Innovation patterns for R&D investor firms in Finland based on an alternative definition of continuous innovators
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Table 8 Alternative VAR(1)-model augmented with time-varying continuous innovation dummy, interaction effects between that dummy and original regressors and control variables
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Table 9 Alternative VAR-model with employment growth and R&D expenditure growth, LAD estimation for continuous versus occasional product innovators
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Table 10 Alternative VAR-model with employment growth and R&D expenditure growth, LAD estimation for continuous versus occasional process innovators
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Table 11 LAD estimation of Eq. (2) across industries and innovation patterns for a VAR(1) model
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Fig. 2
figure 2

Quantile regression plot of the responsiveness of R&D expenditure at time t to sales growth at time t–1 based on the estimation of Eq (2) for our complete sample. Note Confidence intervals (non-bootstrapped) extend to 95 % confidence intervals in either direction. Horizontal lines represent OLS estimates with 95 % confidence intervals. Output obtained using stata module qrqreg of Azevedo (2004). Source Statistics Finland

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Deschryvere, M. R&D, firm growth and the role of innovation persistence: an analysis of Finnish SMEs and large firms. Small Bus Econ 43, 767–785 (2014). https://doi.org/10.1007/s11187-014-9559-3

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