Publications
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"A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications?"; Entropy 2018, https://doi.org/10.3390/e20110887 (registering DOI) Volume 20, Issue 11
In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly… Show more highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms' capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets. Show less
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PDF EU regions and the upgrading for the digital age
In this work we use patent data from the European patent office (EPO) to assess the capabilities of EU regions… Show more in developing digital technologies especially focusing on those that are more closely related to the digital transformation. More specifically, we measure ICT patents by considering those containing digital codes, as defined by the OECD. The penetration of digital technologies in the development of innovative products is instead captured by the co-occurrence of digital and non-digital codes within patent documents; we call these patents ICT-combining patents. Show less
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PDF R&D Intensive corporations and the job market: The danish case
To boost job creation, the labour market role of big multinationals cannot be overlooked. Large R&D investing companies operating in… Show more Denmark act as agents of skill upgrading, rather than destroying mid-skill jobs through job polarisation. However, workers employed by these companies tend to move within such elite (i.e. remaining in the ‘Champion's League') rather than moving to other non-multinational indigenous firms. Scoreboard companies, domestic and foreign, pay higher wages for a given occupation compared to other firms; they also show a higher wage growth. Show less
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PDF Ten-year evolution of EU industrial R&D in the global context
More than a quarter of the industrial investment in global R&D is made by EU companies. In the last decade… Show more EU companies have increased their specialisation in medium-tech sectors, with a significant R&D share increase in the Automobile sector and a decrease in the Aerospace & Defence sector. Industrial dynamics at company level provide insights into policy strategies to strengthen EU corporate R&D and to improve the competitiveness of innovation-driven industries. Show less
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PDF Industrial R&D continued to grow substantially in 2017
R&D funded by the business sector increased in the EU by 5.6%, below the 6.1% global rate and the US… Show more R&D growth (7.2%). The worldwide growth of industrial R&D in 2017 is slightly higher than that recorded in 2016. This growth is largely driven by ICT and health industries. As in previous years, the industrial R&D growth in the EU is led by Germany, with France showing a stronger R&D increase compared to the previous year. In the EU, R&D inflows and outflows for Health industries were nearly equivalent in 2017 (€9.6bn versus €9.4bn) and showed a significant positive trend with respect to 2016. Show less
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PDF 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… Show more 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. Show less
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"Towards evidence-based industrial research and innovation policy"; Oxford Academic. Science and Public Policy, Volume 45, Issue 2, 1 April 2018, Pages 143–150
Calls for better use of scientific evidence to inform policy decisions stem from the belief that enhanced outcomes for the… Show more society can be expected. Yet, the introduction of evidence-based practices in innovation policymaking has not come without criticism. This introductory article sets the scene for the short collection of papers that address specific issues regarding the prospect of better evidence-based policy in the area of industrial research and innovation (IRI). It identifies and discusses key challenges for the transition towards evidence-based IRI policy. It then introduces the three papers, which build upon and depart from related assumptions or narratives reflecting the current state of practices in IRI policy. Show less
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PDF Heterogeneity of technology-specific R&D investments. Evidence from top R&D investors worldwide
In this work, we develop and apply a methodology to estimate technology-specific R&D investments at firm level. To do so,… Show more we combine R&D investment with patent data from the world top R&D investors worldwide and show that investment per patent varies greatly across technologies and across firms developing a given technology. We then use these results to assess the relationship between technology-specific R&D investments and a series of factors characterizing technological development. The estimation strategy makes use of a multilevel framework that allows modelling heterogeneity at the firm and sector level. In line with the literature on the sectoral systems of innovation, we find that sector specificities matter in determining R&D per patent investments, economies of scale in knowledge production, and the cost associated to technological specialization. Moreover, our results suggest that the persistent differences in R&D intensity across firms may be largely related to the technological choices they make. Firms' idiosyncrasies co-exist with significant differences across sectors in shaping knowledge production functions. Implications for policy and research are discussed accordingly. Show less
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PDF From R&D to market: using trademarks to capture the market capability of top R&D investors
This paper investigates the links between the market capability of top corporate R&D investors (EU Industrial R&D Investment Scoreboards), as… Show more captured by trademark data and their economic performance in terms of net sales growth. It provides empirical evidence to better understand the extent to which companies, operating in different industrial sectors, combine technological capabilities with commercialization efforts to generate and appropriate the economic returns of their R&D investments. This paper shows how different dimensions of firms' market capabilities can be captured through trademark indicators. The results suggest that complementing R&D efforts and patenting activities with strong and specific market capabilities can indeed yield significant growth premiums. Moreover, offering services seems to pay off depending on the intensity of R&D investments. Yet, a quantile regression approach and a series of robustness checks indicate that such effects differ across the quantiles of the conditional sales growth distribution. Show less