Abstract
This paper studies the R&D behaviour of fast growing SMEs using CIS III data for 16 countries. We group the countries into three categories of countries having roughly the same stage of technological development. Our first finding is that R&D is more important to high-growth SMEs in countries that are closer to the technological frontier. The second finding is that high-growth SMEs are only more innovative than non-high-growth SMEs in countries close to the technological frontier. This suggests that gazelles derive much of their drive from the exploitation of comparative advantages. From a policy perspective, this indicates that there are important limits to centralising policies that aim to foster high-growth SMEs.
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Notes
Related to this are the findings of Wong et al. (2005). They report using Global Entrepreneurship data that only high-growth potential TEA (Total Entrepreneurial Activity, as measured in the Global Entrepreneurship Monitor) has an explanatory effect on GDP growth rates, whereas necessity TEA, opportunity TEA, and overall TEA have not. This suggests that distinct types of entrepreneurship and growth ambition have a different impact on employment and growth.
As the present paper uses data that allow to calculate one growth indicator, the use of a relative measure of high-growth SMEs is not that important. However, once different time periods can be compared, an absolute definition of gazelles is more useful. For instance, the proposed definition by Ahmad (2006) that defines a high-growth firm as an enterprise with an average annualised growth rate in terms of employees over 20% with at least ten or more employees at the beginning of the observation period. Such a definition would allow to compare shares of high-growth firms across time periods.
We have cleaned the data in such a way that turnin and turnmar become mutually exclusive.
Matching estimators were designed to examine the effect of a treatment. Since we use observational data, i.e., data that are not randomised, and furthermore identify a subset of the sample as being treated, there is no ‘treatment effect’ as discussed in the literature. However, by applying a matching estimator, we control for the characteristics that affect both the quasi-treatment and the response. We deem gazelles to be the ‘treatment variable’, thus using the matching estimator as a tool that produces sophisticated statistics on the difference between gazelle and non-gazelle SMEs.
The software can be downloaded at http://www.werner.hoelzl.wifo.ac.a.
Quantile regressions have three major advantages when compared to OLS: First, quantile regressions allow us to analyse differences in the relationship between the endogenous and exogenous variables at different points of the conditional distribution of the dependent variable. That is, rather than focusing on a specific moment of the distribution, the linear quantile regression is a statistical method that allows us to study the whole range of values of the dependent variable. Quantile regressions allow us to study how one specific quantile of particular interest is correlated with a set of explanatory variables. Extending this analysis to a large number of quantiles, quantile regression allows us to examine how the partial correlation changes across the quantiles. This provides an understanding of the entire shape of the distribution and how it may be shaped by the explanatory variables. Second, the coefficient estimate for the exogenous variable is interpreted in a similar fashion to OLS regression coefficients, namely as the marginal change in the dependent variable due to a marginal change in the exogenous variable conditional on being on the pth quantile of the distribution. Changing estimated coefficients with varying quantiles is indicative of heteroskedasticity issues (Koenker 2005). Third, estimates of the quantile regression are more robust than those of the ordinary least square regression, where the mean value of the dependent variable is predicted. This is especially true in the presence of outliers, as well as for distributions of error terms that deviate from normality.
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Acknowledgements
This paper has been presented at the workshop “Drivers and impacts of corporate R&D of SMEs” in September 2008 at the JRC-IPTS in Seville. I wish to thank the participants, especially Marco Vivarelli (the editor) and Pedro Faria, for comments that helped to improve the paper. The research has been financed by the European Commission under contract no. 022534 (Sectoral Innovation Watch). I wish to thank Erkko Autio, Klaus Friesenbichler, Hugo Hollanders and Andreas Reinstaller for useful comments at an early stage of this research. The usual disclaimer applies.
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Hölzl, W. Is the R&D behaviour of fast-growing SMEs different? Evidence from CIS III data for 16 countries. Small Bus Econ 33, 59–75 (2009). https://doi.org/10.1007/s11187-009-9182-x
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DOI: https://doi.org/10.1007/s11187-009-9182-x