The gains from technology: new products, exports and profits

ABSTRACT

The introduction of new products, greater competitiveness-based export performances, and higher profits are three major benefits from technological change. We build on previous work that has identified ‘virtuous circles’ between R&D, innovation and profits in firms and industries [Bogliacino, F., and M. Pianta. 2013a. “Profits, R&D and Innovation: A Model and a Test.” Industrial and Corporate Change 22 (3): 649–678; Bogliacino, F., and M. Pianta. 2013b. “Innovation and Demand in Industry Dynamics. R&D, New products and Profits.” In Long Term Economic Development, edited by A. Pyka, and E. S. Andersen. Berlin: Springer., Bogliacino, F., M. Lucchese, L. Nascia, and M. Pianta. 2016a. “Modelling the Virtuous Circle of Innovation. A Test on Italian Firms” Industrial and Corporate Change. doi:10.1093/icc/dtw045.] and between R&D, innovation and exports [Guarascio, D., M. Pianta, M. Lucchese, and F. Bogliacino. 2015. “Business Cycles, Technology and Exports.” Economia Politica – Journal of Analytical and Institutional Economics 32 (2): 167–200; Guarascio, D., M. Pianta, and F. Bogliacino. 2016. “Export, R&D and New Products: A Model and a Test on European Industries.” Journal of Evolutionary Economics 26 (4): 869–905.]. We test a model – with a Three Stage Least Squares methodology – using data at the industry level – for 39 manufacturing and service industries – for 6 major EU economies – Germany, France, Italy, Spain, the Netherlands and the United Kingdom – over the period 1995–2011. Results confirm the presence of a ‘virtuous circle’ linking the gains from technology, and identify the specific role of additional variables and the presence of lags and feedbacks. Moreover, the relevance of differences between Northern and Southern European countries is examined, with separate estimations that shed new light on the heterogeneity of these relationships.

KEYWORDS:

• Product innovation
• export
• profits
• three stage least squares
• Europe

JEL CLASSIFICATION:

• F12
• F14
• O31
• O33
• O52

Notes

1. Imported intermediate inputs may have a twofold effect. On the one hand they may embody advanced technology and increase the quality and variety of products, and their technological competitiveness, leading to greater exports. On the other hand, imported intermediates can provide low cost inputs when production is outsourced to low wage countries, increasing industries’ cost competitiveness (Bas and Strauss-Kahn 2014; Hummels et al. 2014; Colantone and Crinò 2014).

2. The formal expression of the Feenstra and Hanson (1996) indicator is the following: where i stands for the industry, j for country and t for time. Foster, Stehrer, and Vries (2013) and Bogliacino, Guarascio, and Cirillo (2016b) has shown that the FHN is strongly correlated with all the available fragmentation measures resulting a reliable measure of offshoring.

3. This evidence gives support to the findings in Guarascio, Pianta, and Bogliacino (2016). In this contribution, a strong correlation between new products and export dynamics emerges if European industries are considered all together. However, such positive association disappear when Southern European cluster is separately considered. For a detailed analysis of the polarization process affecting North and South EU industries see Cirillo and Guarascio (2015).

4. As explained in Section 3, economic and innovation variables are computed as compound annual average growth rates of four years. Accordingly, the change in the FHN indicator is calculated as the simple difference between the last and the first year of each of the considered time windows.

5. The importance of innovation and technological competitiveness strategies in determining exports success has been largely proven both at the sectoral and at the micro level. See, among the others, Bogliacino and Pianta (2013a, 2016b), Dosi, Grazzi, and Moschella (2015a) and Guarascio, Pianta, and Bogliacino (2016).

6. A similar approach has been followed in Guarascio, Pianta, and Bogliacino (2016).

7. The 3SLS method generalizes the two-stage least squares (2SLS) method to take account of the correlations between equations in the same way that Seemingly Unrelated Regression (SUR) generalizes OLS. 3SLS requires three steps: first-stage regressions to get predicted values for the endogenous regressors; a two-stage least-squares step to get residuals to estimate the cross-equation correlation matrix; and the final 3SLS estimation step. The 3SLS method goes one step beyond the 2SLS by using the 2SLS estimated moment matrix of the structural disturbances to estimate all coefficients of the entire system simultaneously. The method has full information characteristics to the extent that, if the moment matrix of the structural disturbances is not diagonal (that is, if the structural disturbances have nonzero ‘contemporaneous’ covariances), the estimation of the coefficients of any identifiable equation gains in efficiency as soon as there are other equations that are over-identified. Further, the method can take account of restrictions on parameters in different structural equations. The simultaneous estimation performed in 3SLS further weakens the potential estimation biases associated with lagged dependent variables with respect to the 2SLS (Zellner and Theil 1962).

8. The choice of using the first lag of both demand variables – combined with the use of long lag differences of four years – is crucial also from an empirical standpoint. In fact, the relation between profits and demand is likely to be affected by simultaneity-related endogeneity. Considering the development of the regressors at their first lag, thus, helps to achieve consistency in the estimations.

9. The rationale behind the proposed grouping relies on the ongoing dynamics of polarization in Europe documented in Section 3. Similar grouping, however, have been proposed in a number of a studies analysing the process of European divergence between Central European and Mediterranean economies (for a review see Cirillo and Guarascio 2015).