Abstract
The links between three interconnected elements of the Schumpeterian sources of economic change are explored, conceptually and empirically, and related to the role played by demand factors. First, we examine the commitment of industries to invest profits in cumulative R&D efforts; second, the ability of industries’ R&D to introduce to new products in markets; third, the impact of new products on entrepreneurial profits. We consider the nature and variety of innovative efforts—distinguishing in particular between strategies of technological and cost competiveness—and we introduce the role of demand in pulling technological change and supporting profits. We develop a simultaneous three-equation model and we test it at industry level—for 38 manufacturing and service sectors—on six European countries over two time periods from 1994 to 2006. The results show that the model effectively accounts for the dynamics of European industries and highlights the interconnections between the different factors contributing to growth.
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- 1.
“Whence come the sums needed to purchase the means of production necessary for the new combinations if the individual concerned does not happen to have them? (…) By far the greater part (…) consists of funds which are themselves the result of successful innovation and in which we shall later recognise entrepreneurial profit” (Schumpeter 1955, 71–72). See also O’Sullivan (2005).
- 2.
- 3.
Some studies have tried to explore the relationships of (2) using patents as a measure of product innovation; a review can be found in Denicolò (2007). However, a large literature has shown that patents are a biased indicator and capture very poorly the innovation output outside Science Based industries (for a discussion on measuring innovation, see Archibugi and Pianta 1996; Smith 2005).
- 4.
CIS data are representative of the total population of firms and are calculated by national statistical institutes and Eurostat through an appropriate weighting procedure. Economic variables are deflated using the GDP deflator from Eurostat (base year 2002) corrected for PPP (using the index provided in Stapel et al. 2004).
- 5.
We use a variable of objective and not a direct measure of demand for two reasons: first, given the time lag necessary to obtain results from R&D, putting a contemporaneous term would be meaningless; second, the inclusion of a future term would be seriously affected by endogeneity problems and would have implied some form of rational expectations which are unrealistic in a radical uncertainty domain.
- 6.
See Bogliacino and Pianta (2012) for a discussion of this variable. For every observation (sector-country) we calculate the labour productivity (value added per employee) in the initial year of the sub-period. Then for each industry we individuate the leader (e.g. for sector x1 the highest labour productivity is in country y2) and we compute the distance in percentage points. At the industry level this variable may be affected by the pattern of countries’ competitive advantages; unfortunately with our dataset it is the only available measure.
- 7.
A systematic analysis of the links between innovative dynamics, demand factors and structural change is in Lucchese (2011).
- 8.
We remind also that, technically, the effects captured through country dummies cannot be identified; since our unit of analysis is the industry, which are in fixed numbers, the only way to increase the number of observations is by increasing the number of countries. Asymptotically, the number of country effects diverges at the same rate as the sample size, thus we would face an incidental parameter problem. As a result, we do not report these estimations. All three robustness check regressions are available from the authors upon request.
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Appendix
Appendix
In order to appreciate the relevance of the inclusion of demand variables in our results in Table 3, we report in Table 4 the results of a different estimate that excludes the proxies for demand and considers other variables only. The structure of results is the same as in Table 3.
In the first equation, as expected, R&D is path dependent, is pulled by demand, and is finance constrained, with profits playing a supporting role. The only coefficient that does not meet our expectation is the distance from the frontier which is not significant. In order to explore this variable a graphical examination is provided below
In the second equation product innovation is driven by lagged R&D alone. In the third equation product innovation and the adoption of new technology, together with sales growth, explain the variance of the growth rate of profits
These results are consistent with those found in the previous version of our model (Bogliacino and Pianta 2012), and with those of Table 3 above. The inclusion of demand variables strengthens the explanation of new products in (2)
In (1) the distance from the frontier of labour productivity does not emerge as significant (the same is in Table 3 above). In order to explore in greater detail this variable, we can examine it graphically. If we regress R&D per employee on its lag and we take the residuals, we can plot their distribution for different intervals of the distance. In order to choose the threshold for the distance from the frontier variable, we first look at the distribution of the distance and we see that it is bimodal, with a first mass of probability between 0 and 20 %. Then we plot the empirical density of the residuals for the distance from the frontier below and above 20 %. The results are shown in Fig. 1 below. As we can see from the graph, for distances lower than 20 % (closer to the frontier) there is higher R&D expenditure and—one would say—higher right tail skewness. However, for distances less than 20 % there is also much more variability in the distribution of R&D expenditure. This evidence contributes to explain the lack of significance for this variable in the model
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Bogliacino, F., Pianta, M. (2013). Innovation and Demand in Industry Dynamics: R&D, New Products and Profits. In: Pyka, A., Andersen, E. (eds) Long Term Economic Development. Economic Complexity and Evolution. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35125-9_5
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