Export, R&D and new products. a model and a test on European industries

Abstract

In this article we extend the model developed by Bogliacino and Pianta (Industrial and Corporate Change 22 649, 2013, b) on the link between R&D, innovation and economic performance, considering the impact of innovation on export success. We develop a simultaneous three equation model in order to investigate the existence of a ‘virtuous circle’ between industries’ R&D, share of product innovators and export market shares. We investigate empirically – at the industry level – three key relationships affecting the dynamics of innovation and export performance: first, the capacity of firms to translate their R&D efforts in new products; second, the role of innovation as a determinant of export market shares; third, the export success as a driver of new R&D efforts. The model is tested for 38 manufacturing and service sectors of six European countries over three time periods, from 1995 to 2010. The model effectively accounts for the dynamics of R&D efforts, innovation and international performance of European industries. Moreover, important differences across countries emerge when we split our sample into a Northern group – Germany, the Netherlands and the United Kingdom – and a Southern group – France, Italy and Spain. We find that the ‘virtuous circle’ between innovation and competitiveness holds for Northern economies only, while Southern industries fail to translate innovation efforts into export success.

Appendix

1.1 Descriptive statistics

1.1.1 Export market shares

The six countries contained in our sample account for 95 % of the EU-15 and for the 70 % of the EU-28 exports. The percentages are obtained dividing the sum of the exports of the considered countries by the total EU exports. Data are synthetized in Table 10. Moreover, the export performance of the countries in our sample (measured by the export market shares) remain stable when different data sources are considered (calculations based on our SID database are compared with Eurostat data) and when exports are distinguished by destination. These elements bring us to consider the export market share variable used in the third equation of the system as a reliable proxy for industries performances in terms of international competitiveness.

Table 10 Intensity in the use of domestic and imported inputs by country (1995-2010)

1.1.2 Innovation vs performance variables

The following scatter plots relate product innovation with export and value added as proxies for economic performance; they identify countries’ heterogeneity and sectoral regularities, using Pavitt classes. Figure 2 shows a clear correlation between R&D efforts and innovation performance; Science Based and Supplier Specialized industries - those sectors for which innovation is most important – are located at the top right of the graph, as expected. Within technological intensive sectors, the best performers are from Northern countries and France. Figure 3 relates product innovation and exports and also, in this case, the correlation between the two variables is detected. As in Fig. 2, sectors with a higher technological intensity (SB and SS) are positioned in the top right of the graph and the North–south divide is again clear in the distribution.

Fig. 3

Product innovation vs exports by country and Pavitt categories

Fig. 4

Product innovation vs value added by country

Figure 4 provides a cross country comparison plotting product innovation versus the rate of change of value added over the sampled period. In this graph, the North–south divide is again clear, as well as the role of Germany as an outlier. The descriptive evidence provided is the background to the results of the econometric model developed in section 5.

1.1.3 Intermediate inputs

The final step of this data inspection regards the role played by intermediate inputs, distinguishing them in terms of technological content and source. Table 11 reports the share of each intermediate input over total industry production of countries. The numbers in Table 11 depict a situation where there is little variability across countries; even the high tech imported inputs have a highly stable relevance across countries. Conversely, domestic low tech intermediate inputs play a major role in Southern countries, and Italy in particular, where their share over total production is 32 %, ten point higher than the Northern average.

Table 11 Intensity in the use of domestic and imported inputs by revised Pavitt category (1995-2010)

The final Table 12 reports the intensity in the use of domestic and imported inputs by Pavitt Categories for the period 1995-2010. As expected, the variability across Pavitt Categories is higher than the one observed among countries. Sectors belonging to Science Based and Supplier Specialized categories rely mostly on high tech intermediate inputs, and their openness to the foreign market is also remarkable. Conversely, Scale Intensive and Supplier Dominated sectors are characterized by an intensive use of low tech inputs originating principally from the domestic market. A substantial divergence in terms of economic and innovative performances across our sample’s countries emerges from this first data inspection. Moreover, technological factors turn out as a crucial element in the explanation of competitiveness for the EU countries we have considered.

Table 12 The R&D equation

Table 13 The Innovation equation

Table 14 The export market share equation

Table 15 R&D equation

Table 16 Innovation equation

Table 17 Innovation equation

Table 18 The R&D equation

Table 19 The Innovation equation

Table 20 The export market share equation

1.2 Model diagnostics

1.2.1 Dummy variables estimations

In this subsection, we report the estimations, equation by equation including the dummy variable coefficients. The dummy variable coefficients have been discussed in Section 5.1.

The standard diagnostic tests are examined here, equation by equation. We try to detect the presence of heteroscedasticity and/or multicollinearity. To check we respectively use a Breusch-Pagan test and a Variance Inflation Factor (VIF, calculated on the baseline WLS regression). In order to address the endogeneity issue, we regress the explanatory variables over a set of instruments, compute the residuals and re-run a robust standard errors-WLS of the equation, with the residuals included as an explanatory variable. The T-test for the coefficient of the residuals included becomes a test of endogeneity; see Wooldridge (2002, p. 118).

The results are the following: we have to estimate robust standard errors since the Breusch-Pagan test rejected the null hypothesis of homoscedasticity, explanatory variables are orthogonal to the error term, and multicollinearity is not an issue; usually VIF is considered worrisome if it is higher than four (or higher than ten, according to different sources), and these thresholds are four to ten times higher than the value of our sample statistics. We cannot reject our formulation of WLS with robust standard errors. Regarding endogeneity (we developed standard endogenity tests on SIZE, EXPGR, MACH and INT_IMP_HT), our diagnostic rejects the hypothesis of endogeneity for the tested variables.

To go a step further in the endogeneity test, we report the results of the 2SLS estimation of each of our baseline equations (as already illustrated above, we instrumented our regressors suspected of being endogenous with their lag, the rate of change of value added, country dummies, time dummies and Pavitt dummies).

The basic formulation of the R&D (1) and New products (2) equations is not rejected by the results and the overidentification test supports the validity of the selected instruments. As a final step, we report the results of the 2SLS estimation for the third equation of the system.

Also in this case, our formulation is consistent with the data and the instruments are properly selected.