Forms of extramural research acquisition and product innovation: Data from econometric estimations

This Data article provides a collection of Data and econo- metric estimates of the relationship between various forms of external research and Development (R&D) acquisition and product innovation. Specifically, the Data are elaborations on Eurostat (2015) and the EFIGE (2015) survey. Data relate to research acquired by external firms inside the group to which a company belongs, universities and research centres, and other companies. The Data presented here are additional in- formation and analysis to the article of Carboni and Medda [1] . Data derive from econometric applications on the in- formation contained in a survey of 13,621 European man- ufacturing firms. The econometric framework considers: ( 1 ) Potential non-linear effects of the age of firms on product innovation; ( 2 ) Geographical variation of innovative activity by the inclusion of 137 regional dummies (NUTS-2-level); ( 3 ) Intersectoral differences by the inclusion of 117 indus- trial dummies (3-digit NACE). We employ systems of equa- tions regressions to take simultaneity end endogeneity into account. For this purpose, the model identification is ac- complished through the use of a reduced form equation for R&D with two instrumental variables (IV) aimed at capturing regional technological environment. Specifically, we use an instrumental variable framework to compute the impact of external research on (1) the probability of implementing product innovations and (2) the market success of product innovations. The latter is measured by the share of total turnover of innovative products sales. Special focus is put on the potential role of the regional technological environ- ment. For the computations we used the cmp command in STATA, which builds upon the maximum simulated likeli- hood analysis. The models are also estimated using the frac- tional response technique to check the 0-100% bounded na- ture of this variable. The Data presented here can be useful for companies to better design R&D strategies aimed at im- proving firms’ organization, synergies, and growth. This may help strategic decision making, and a more efficient coordi- nation of the complex process of production. Data are also useful for policy makers for designing public R&D schemes, both at the national and at the European level. Finally, the Data represent a useful starting point for future research con- cerning the proprietary structure of the firm and the work- force, innovation and R&D, internationalization, finance, and market.