Global Innovation Networks: State of the art and issues at stake for GVCs
The objective of the study on "Literature review on Global Innovation Networks: State of the art and issues at stake for GVC" is to summarise the state of the art literature on Global Innovation Networks (GINs) in order to understand the patterns and evolution of these networks. Based on the review of the literature the study develops a conceptual framework on the relationship between GINs and global value chains (GVCs). The framework systematises the main commonalities and differences between GINs and GVCs and makes suggestions for further evidence collection to address the links between GINs and GVCs.
Scientific Publication Activity of Scoreboard Companies
Daniele Rotolo, Roberto Camerani
This report examines the extent to which firms included in the 2014 EU Industrial R&D Investment Scoreboard are involved in publication activity. The Scoreboard includes 2,500 firms most active in terms of R&D expenditure. These firms account for about 90% of the global private R&D expenditure. On the basis of a novel methodological approach to collect publication data from the Web of Science (WoS) for all Scoreboard firms and their subsidiaries (about 570,000 subsidiaries), the report examines the Scoreboard firms' publication activity for the 2011-2015 period. The main findings are summarised below:
• Scoreboard firms (and their subsidiaries) contributed to 314,411 publications (about 3% of the global publication output as reported in WoS);
• About 84% of Scoreboard firms contributed to at least one publication (with an average about of 137 publications per firm); the distribution of number of publications by firm is, however, highly skewed;
• There is a relatively strong correlation between firms' R&D expenditure and their number of publications, but firms that score well in the rank by R&D expenditure (i.e. Scoreboard) do not necessarily score well in the rank by number of publications;
• 58% of Scoreboard firms' publications involve at least one academic institution;
• About 12% of Scoreboard firms' articles are within the top 10% most cited articles, and about 45% of Scoreboard firms contributed to at least one article that is within the top 10% most cited articles.
A reappraisal of the impact of corporate R&D and innovation on employment
The aim of this report is to provide updated quantitative and qualitative analyses of the impact of corporate R&D and innovation on employment in industries and firms in the European Union member states. Over the last decades, the paradigm based on ICT and automation has led to a dramatic adjustment of the employment structure raising again a widespread fear of an upcoming "technological employment". After a critical survey of the more updated empirical evidence on the topic, new econometric analyses (longitudinal data), based on a dynamic labour demand, are provided. The first one is at the sectoral-level and uses OECD STAN-ANBERD data; the second one is at firm-level and uses European R&D top-performers Scoreboard data. In addition, two microeconometric studies, based, respectively, on Italian and Spanish firm-level data, are provided. Finally, in order to offer evidence of the "qualitative" impact of innovation, a tentative study matching, at the sectoral-level, OECD STAN-ANBERD and EU-SILC data, has been provided. Overall, R&D seems to have a positive and significant impact on employment, especially in high-tech industries. Moreover, R&D positively affects the categories of tertiary educated workers, high-skilled white-collars and the employees handling non-routinized tasks.
Study on tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables
Dominik Janzing, from the Max Planck Institute for Intelligent Systems, recently finished a subcontracted report entitled "Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables." The machine learning community has recently developed some exciting new techniques for causal inference from observational data, which work even for discrete variables. In this project, Dominik applied these techniques to analyse Scoreboard data (on the world's largest R&D investors) as well as Community Innovation Survey data. The report contains a large number of results, and also makes the software available for future research.