Abstract
This study investigates the effect that spending in on-the-job training directly aimed at developing and/or introducing innovation and skilled human capital has on innovative sales. In particular, it investigates whether or not the returns on these investments differ between small and medium-sized enterprises (SMEs) and large firms, and the extent to which returns are affected by a firm’s knowledge intensity. Using data from the third Community Innovation Survey, covering 23 European countries, this paper estimates a system of three equations in which investments in training and in the stock of R&D personnel are treated as endogenous in relation to the amount of innovative sales on which they are presumed to have an effect. Empirical evidence confirms that investments in training and in the stock of R&D personnel have a positive effect on firms’ innovativeness and that returns on them are not affected by the degree of knowledge intensity of the firm. However, the returns are always statistically significantly higher in large firms than in SMEs.
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Source: Authors’ elaboration on CIS3 data
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Notes
To this end, I borrowed the Eurostat concept of knowledge-intensive activities (KIA) (Eurostat NACE Rev. 2 definition), which are identified by considering the educational attainment of the workforce, and I defined all firms in which over 33% of the workforce is educated to tertiary level as knowledge intensive.
If skills are in short supply, a firm may decide not to invest in technologies for which a high level of human capital is necessary.
I need to thank an anonymous referee for their suggestion to clarify this point.
The research was carried out at Eurostat’s Safe Centre in Luxembourg.
In the empirical literature on the impact of human capital on firms’ performance, the most common proxies used for this category of intangibles are labour costs (Lin 2007), the level of education of the workforce (Crepon et al. 1998; Loof and Heshmati 2002; Aiello and Pupo 2004), the number of researchers and the level of training.
In Crepon et al. (1998) there was a third block, namely the link between innovation inputs and firms’ productivity.
Innovative firms represented 37% of the overall CIS sample of European firms, that is, 32,583 enterprises out of 87,340.
Firms that reported zero turnover or zero employees were removed from the original dataset. Among innovative firms, successful product innovators during the period 1998–2000 represented 77%.
It is not possible to know the extent to which this training is aimed at R&D workers and to which it is aimed at other workers, such as administrative staff who need to learn to use new accounting software for a new product line.
Among the firms that declared a positive expenditure on training activities, 5134 had engaged in training, marketing and design activities and 4125 stated that they had engaged in training activities only. In addition, 2701 had engaged in training and marketing activities but not in design activities, whereas 1487 had engaged in training and design activities but not in marketing activities.
There is another source of measurement bias, which probably implies an underevaluation of the firms’ total investment in training (and not of the number of firms investing, as in the previously discussed case), as spending on firm, specific human capital consists of two types of expenses (Corrado et al. 2005): the amount and the time spent on training. Given the information available and the data used, I was able to consider only the former.
As a robustness check, the system of equations was also estimated using R&D total investment (intramural and extramural R&D) instead of the amount of R&D personnel as a proxy for innovative input. The significance and signs of the variables of interest did not vary.
In this regard, it is true that the appropriate procedure would be to model the decision that has produced the zero observations, rather than using the Tobit model mechanically. However, the nature of the dependent variables and the database used in this analysis did not allow this modelling option.
Preliminary checks for multicollinearity were performed and a high value of correlation was found for R&D investments and R&D personnel, hence the exclusion of R&D investments from the determinants of the innovation output equation.
The results of the robustness checks are available upon request.
The choice between the two strategies is likely to be influenced by institutional variables (i.e. the extent of friction in the labour market) and/or depend on the skills supplied by the labour market, on the age of a firm’s workers and their education, and on the employment structure of the firm itself (e.g. in terms of the proportion of tenured to temporary jobs).
Unfortunately, this is the only item of information requested in the CIS questionnaire and, given the restrictions operating at the Eurostat Safe Centre, introducing other external controls was been authorised.
I have not used the number of employees with tertiary education for the high correlation with the size control inserted as a common regressor in all the equations of the system.
Many authors find that cooperating firms spend more on R&D (see, for instance, Mairesse and Mohen 2010).
Their introduction determines a significant drop in the number of observations, but, given their established relevance and importance, and the fact that their exclusion did not affect the direction or significance of the results, I preferred to leave them in the model.
I did not insert the amount of investment in R&D for two reasons. First, it is highly correlated with the latent RDpers*, as they are both proxies for the innovation effort of a firm; second, given its endogeneity, this would have required the addition of another equation to the system, which anyway would not have solved the correlation issue with the main variable of interest.
As this variable may be endogenous, further checks have been performed. Overall, its inclusion or exclusion does not affect the robustness of the results.
In other words, McDonald and Moffit (1990) showed that a change in the independent variable x has two effects: it affects the conditional mean of y in the positive part of the distribution (2), and it affects the probability that the observation will fall in that part of the distribution (1). The sum of both effects gives the unconditional effect (3).
Another example of a fixed cost associated with the provision of training would be, for example, the cost associated with the design of a training plan or the evaluation of a firm’s training needs.
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Ciriaci, D. Intangible resources: the relevance of training for European firms’ innovative performance. Econ Polit 34, 31–54 (2017). https://doi.org/10.1007/s40888-016-0049-8
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DOI: https://doi.org/10.1007/s40888-016-0049-8