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A bit of basic, a bit of applied? R&D strategies and firm performance

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Abstract

Most studies analysing the relationship between R&D and firm growth focus on total R&D investment. This paper aims to analyse separately each component of R&D investment (basic research, applied research and technological development) and evaluate how these types of R&D investment are related to firm growth. Using a sample of 3972 Spanish manufacturing firms during 2004–2015, our empirical results are the following. First, firms have heterogeneous R&D strategies. The common wisdom that young firms invest in basic research, while old firms invest in applied research, is not supported in our data. Second, we investigate the characteristics and dynamics of firms with different R&D strategies. We observe complementarities between applied research and technological development due to their positive associations with firm growth. Finally, our results show that there is a tendency for firms to transition from basic research to applied research.

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  1. Despite efforts to analyse the determinants of firm growth, firm growth seems near-random and difficult to predict (Bottazzi et al. 2002; Coad 2009). Among the determinants, the analysis of R&D investments and its impact on firm growth has been of interest for academics, managers and policy makers for a long time (Mansfield 1980; Hall 2002).

  2. Such initiatives include collaborative programs between public agents and firms, the promotion of the transition of employees between science and industry, and the development of specific funding programs.

  3. With a short sample of US firms, Mansfield (1980) assessed the proposition that firms’ basic research is a significant determinant of productivity growth.

  4. Rosenberg (1990) stated that the output of basic research is some form of new knowledge that may be used to play some further role in the development of new products.

  5. The 2009 wave provides information on age. Since we can track firms back in time from 2009, we are able to impute firm age for previous years.

  6. A possible limitation of the data could be that firms cannot accurately distinguish between basic and applied research, in line with insights from Nelson (1959, p. 300): "the line between basic scientific research and applied scientific research is hard to draw."

  7. The distribution is less skewed towards the right when the oldest firms are considered. Kolmogorov–Smirnov tests show that the null hypothesis of equality for the distribution is strongly rejected for any two contiguous age classes. The only exception is basic R&D for age groups 11–20 and 21–30 years.

  8. See for example García-Quevedo et al. (2017) for the incidence of demand barriers on R&D (and also firm age, firms’ size, group membership and exporting); see D’Este et al. (2012) on barriers to innovation, Crépon et al. (1998) for a general model of the determinants of R&D, and more recently Audretsch et al. (2020).

  9. The use of labour productivity only captures the ratio between sales and one of the productive factors, employees. Thus, we do not take into account changes of other factors. Total Factor Productivity (TFP) has been used to address this problem, where data allows. However, estimating TFP is no easy task due to the potential empirical problems related to measurement error, and also endogeneity and simultaneity bias.

  10. Measuring firm performance indicators is not an easy task. Among the different indicators, we may highlight employment, sales, added value, profits, assets, total factor productivity and labour productivity. However, there is a consensus pointing to sales and productivity as two of the most suitable indicators to capture firms’ patterns (García-Manjón and Romero-Merino 2012).

  11. External R&D investment are expenditures to acquire R&D outside the company by contract or agreement. According to PITEC, all the expenditures must be a direct purchase of R&D.

  12. The analysis of the distribution show that the coefficient becomes significant and positive for high-growth firms in terms of sales for quantile 95% and in terms of productivity for quantile 75%. Results are available upon request to the authors.

  13. The coefficient of investment in technological development becomes positive for high-growth firms in terms of sales (quantile 95%) and employment (quantiles 75 up to 95%). However, the coefficient is negative in terms of productivity (10%, 50% and 75%). Results are available upon request to the authors.

  14. In this vein, Bravo-Ortega and Marin (2011) point out that most empirical studies assessing the R&D–productivity relationship fail to consider the possible simultaneity of these variables.

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Acknowledgements

We are grateful to Giulio Bottazzi, Patrick Llerena, Andrea Mina, Alessio Moneta, Ammon Salter, Federico Tamagni, Marco Vivarelli, Mehmet Ugur, and participants at the Workshop on “Innovation, firm dynamics, employment and growth: New developments in modelling and estimation” (Greenwich, UK, 21 June 2019), the 5th online workshop on Industrial Dynamics and Innovation (13 December 2019), as well as seminar participants the Sant’Anna School of Advanced Studies (Pisa, Italy) and at Universitat Rovira i Virgili (Reus, Spain), and the Editor (Al Link) and an anonymous referee. We are grateful to Verònica Gombau for her research support. This paper is part of a research project carried out with the financial support of Universitat Rovira i Virgili (2019PRF-URV-B2-80), the Consolidated Group of Research 2014-SGR-1395. The usual disclaimers apply.

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  1. Waseda Business School, Waseda University, Bldg. 11 1-6-1 Nishi-Waseda, Shinjuku, Tokyo, 169-8050, Japan

    Alex Coad

  2. GRIT & ECO-SOS, Department of Economics, Universitat Rovira I Virgili, Av. Universitat, 1, 43204, Reus, Spain

    Agustí Segarra-Blasco & Mercedes Teruel

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Coad, A., Segarra-Blasco, A. & Teruel, M. A bit of basic, a bit of applied? R&D strategies and firm performance. J Technol Transf 46, 1758–1783 (2021). https://doi.org/10.1007/s10961-020-09826-1

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