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High-growth firms in changing competitive environments: the US pharmaceutical industry (1963 to 2002)

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Abstract

Firms across sectors and regions are highly skewed in their ability to engage with innovation and even more skewed in their ability to translate investments in innovation into higher growth. Recent attention has been placed on the importance of ‘high-growth firms’ (HGF) for innovation policy. Our paper explores under what conditions HGF matter for translating R&D investments into economic growth and how this depends on firm-specific and industry-specific factors. We use quantile regression techniques to study the R&D–growth relationship in HGF compared to low-growth firms. Unlike previous studies, we pay particular attention to whether this relationship depends on the particular period in the industry’s life cycle. We focus on the US pharmaceutical industry from 1963 to 2002 and find that the R&D–growth relationship is sensitive to the changing competitive environment over the industry’s history, which suggests that innovation policy must focus not only on firm attributes but also competitive structures.

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Notes

  1. We address our own precise definition and use of the term HGF in Sect. 3.2.

  2. One exception is Falk (2010) whose results indicate R&D intensity had the strongest influence around the median conditional distribution of employment growth for Austrian firms between 1995 and 2006, although fast growers still generally benefit from R&D.

  3. Based on estimates of global pharma sales from IMS (2007).

  4. We make use of both the COMPUSTAT-reported acquisition data and our own analysis of M&A activity to perform robustness checks on our model estimates reported in Sect. 5.

  5. For comparability, we report statistics using the sample applied in our quantile regression model (described in Sect. 4), which includes only those firms with greater than 5 years of consecutive observations. The sample used in the quantile regression analysis is restricted to 1963–2002 because of the unreliability of patent data in the 1950–1962 and 2003–2007 periods, as noted in Sect. 3.1. Regressions run on the 1951–2007 period without including the variable patenting persistence give a similar pattern of results to those described in Sect. 5.

  6. We check our model (reported in Sect. 5) using 3 year logged differenced growth rates and find similar results compared to using the one period measure however because of the lag structure of model used in our estimations it does significantly reduce the sample size, excluding firms with less than 8 years of data. If our analysis is to help reveal the characteristics of all high growth firms and inform policy, it is important to avoid excluding firms, as policy makers cannot know ex-ante which firms will fail.

  7. The six individual measures of R&D have positive loadings of roughly equal size (between 0.28 and 0.5) in the first component.

  8. For firms present in the first year of COMPUSTAT, we manually adjust their age to account for their date of flotation.

  9. R&D scale is strongly correlated with annual R&D expenditure (corr = 0.78); however, rather than investigate the effect of small changes in R&D expenditure on growth, we investigate the extent to which above average annual R&D expenditure has on growth—therefore giving a relative R&D performance measure. R&D scale also has a weak, but negative correlation with R&D intensity.

  10. Note here market is defined as the total annual sales of COMPUSTAT firms.

  11. The HTI (Hall and Tideman 1967), an alternative to the HHI (HHI is calculated as a sum of the squares of market share for each Pharma firm), includes information on the number of firms in the concentration index and takes into account entry.

  12. Here, we are not arguing that there should be a specific date or breakpoint in the competitive regime, but that the pre/post-1980 distinction is a useful method to group the data to capture differences in the competitive environment faced by pharma over time.

  13. See supplementary online material for details.

  14. Similar to Danzon et al. (2007), we look for the effect of M&A on subsequent firm performance, as well as in the acquisition year.

  15. Note, lags of R&D intensity, R&D scale, Sales, VC, and Persist were tested, but were either highly collinear or statistically insignificant and could be dropped without implication to the results reported.

  16. In fact, adjusting this restriction to allow for firms with between a minimum of three (without additional lags in the model) and 10 years of history yields very similar estimation results to those reported in Sect. 5 for R&D intensity and VC.

  17. Regressions run on the 1951–2007 period without patenting persistence give a similar pattern of results for the remaining coefficients to those described in Sect. 5.

  18. Our results for R&D intensity are robust to dropping R&D scale.

  19. See discussion of Schumpeter Mark 1 and Mark 2 in Sect. 2.3.

  20. It is important to note that the presence of VC is much stronger post-1980, although the VC activity present pre-1980 is found to have a statistically significant and positive influence on growth.

  21. If we exclude R&D scale and VC, we find evidence that Persist is statistically significant at growth quantiles above 50 %.

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Acknowledgments

The research leading to these results was funded by an INET grant on Financing Innovation (INO1200037).

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Authors and Affiliations

  1. University of Sussex, Brighton, BN1 9RH, UK

    Mariana Mazzucato

  2. The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK

    Stuart Parris

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  1. Mariana Mazzucato
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Correspondence to Mariana Mazzucato.

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Mazzucato, M., Parris, S. High-growth firms in changing competitive environments: the US pharmaceutical industry (1963 to 2002). Small Bus Econ 44, 145–170 (2015). https://doi.org/10.1007/s11187-014-9583-3

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