Abstract
The existence of R&D spillovers or externalities i.e. the effects of firms’ research activities on other firms activities was theoretically established by Arrow 1962, but few empirical studies have addressed their effects on firms’ economic performance (i.e. value-added) and technological performance (innovation output). In an open economy, firms’ economic and technological performances depend on the position of these firms in their national and international technological environments. The main focus of this article is identifying the different channels through, which spillover occurs, specially the international technology spillovers (i.e. R&D activities of foreign firms; foreign technology payments; international intermediate inputs; and international R&D cooperation) and the mobility of engineers and scientists between firms. Our statistical and econometric analysis determines that spillovers drive the production of individual firms together and link it to the incidence of innovations. Thus, using a pooling method based on segmentation of bunched (or grouped) individuals rather than those of usual individuals panel models and proposing an efficient new full information method (3SLS),1 this empirical study shows that international spillovers are rather large compared to national spillovers. They account for a substantial fraction of the variation in firm production and innovation output in the French economy. The mobility of engineers and scientists help a firm to acquire knowledge externally so as to innovate and increase its production. The effects of technological policy tools used by the French government on the innovation and production are rather very high and incite firms to increase their R&D efforts. This study also demonstrates the existence of a potential simultaneity in the decision to impliment R&D and the spillover pools. The estimated coefficients obtained for the classical variables (capital and employment) are comparable to those obtained in the literature. * I dedicate this article to the unforgettable Professor Pietro Balestra. 1 The 2SLS estimators of a Random Coefficients Models are in our article with Prof. Pietro Balestra (cf. Balestra and Negassi, 1992).
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