Innovation and Demand in Industry Dynamics: R&D, New Products and Profits

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.

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.

Table 4 The system: baseline formulation three stage least squares

Fig. 1

The density of R&D for different value of the capability proxy

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