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on Technology and Industrial Dynamics |
| By: | Nils Grashof; Stefano Basilico; |
| Abstract: | This study explores the regional diversification processes into green technologies (2000- 2017) and their implications for regional inequalities. Utilizing patent and Eurostat data, we analyze these processes along the economic strength of regions and the nature of their knowledge base. Our findings reveal that both structurally strong and weak regions can successfully diversify into green technologies if they possess related technological capabilities. However, brown regions cannot do so. Already existing patterns of divergence between these two types of regions are unlikely to be exacerbated by a green transition, but new regional disparities between brown regions and other regions could emerge. |
| Keywords: | dark side of innovation, inequality, regional diversification, regional inequality, green innovation, green transition |
| JEL: | O32 O33 R11 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2314&r=tid |
| By: | BOSE Udichibarna; GREGORI Wildmer; MARTINEZ CILLERO Maria (European Commission - JRC) |
| Abstract: | Our study explores the implications of technological shifts towards greener and sustainable innovations on acquisition propensity between firms with different technological capacities. Using a dataset of completed control acquisition deals over the period of 2009-2020 from 23 OECD countries, we find that green acquirors (i.e., firms with green patents) are more inclined to enter into acquisition deals with green firms due to their technological proximity and informational advantages. However, after the Paris Agreement, green acquisitions by non-green acquirors increased especially from those in climate policy-relevant sectors and based in countries with low environmental standards. We also find that green acquisitions after the Paris Agreement do not show any significant impact on their post-acquisition innovation performances, raising concerns related to greenwashing behaviour by investing firms. |
| Keywords: | Acquisitions, Green patents, Firm Innovation, Paris Agreement, Green Transition |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc133600&r=tid |
| By: | Sofia Patsali (Université Côte d'Azur, France; CNRS, GREDEG); Michele Pezzoni (Université Côte d'Azur, France; CNRS, GREDEG; Observatoire des Sciences et Techniques, HCERES, France; ICRIOS, Bocconi University, Italy); Jackie Krafft (Université Côte d'Azur, France; CNRS, GREDEG) |
| Abstract: | In line with the innovation procurement literature, this work investigates the impact of becoming a supplier of a national network of excellence regrouping French hospitals on the supplier's innovative performance. It investigates whether a higher information flow from hospitals to suppliers, proxied by the supply of AI-powered medical equipment, is associated with higher innovative performance. Our empirical analysis relies on a dataset combining unprecedented granular data on procurement bids and equipment with patent data to measure the firm's innovative performance. To identify the firm's innovative activities relevant to the bid, we use an advanced neural network algorithm for text analysis linking firms' equipment descriptions with relevant patent documents. Our results show that firms becoming hospital suppliers have a significantly higher propensity to innovate. About the mechanism, we show that supplying AI-powered equipment further boosts the suppliers' innovative performance, and this raises potential important policy implications. |
| Keywords: | Innovation performance, public procurement, medical equipment, hospitals, artificial intelligence |
| JEL: | H57 D22 O31 C81 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2023-05&r=tid |
| By: | Alexander P. Frankel; Joshua L. Krieger; Danielle Li; Dimitris Papanikolaou |
| Abstract: | We examine the role of spillover learning in shaping the value of exploratory versus incremental R&D. Using data from drug development, we show that novel drug candidates generate more knowledge spillovers than incremental ones. Despite being less likely to reach regulatory approval, they are more likely to inspire subsequent successful drugs. We introduce a model where firms are better able to evaluate the viability of incremental drugs, but where investing in novel drugs helps firms learn about future projects. Firms appear to put more value on evaluation versus learning, and those patterns are in-part driven by the appropriability of spillovers. |
| JEL: | G11 L65 O31 O32 O34 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31290&r=tid |
| By: | GRASSANO Nicola (European Commission - JRC); HERNANDEZ GUEVARA Hector (European Commission - JRC); FAKO Peter (European Commission - JRC); NINDL Elisabeth (European Commission - JRC); GEORGAKAKI Aliki (European Commission - JRC); INCE Ela (European Commission - JRC); NAPOLITANO Lorenzo (European Commission - JRC); RENTOCCHINI Francesco (European Commission - JRC); TUEBKE Alexander (European Commission - JRC) |
| Abstract: | The main objective of the EU Industrial R&D Investment Scoreboard (the Scoreboard) is to benchmark the performance of EU innovation-driven industries against major global counterparts and to provide an R&D investment database that companies, investors and policymakers can use to compare individual company performances against the best global competitors in their sectors. The 2022 edition of the Scoreboard analyses the 2500 companies that invested the largest sums in R&D worldwide in 2021. These companies, with headquarters in 41 countries, and more than 900k subsidiaries all over the world, each invested over EUR 48.5 million in R&D in 2021. The total investment across all 2500 companies was EUR 1093.9 billion - an amount equivalent to 86% of the world’s business-funded R&D and passing the trillion Euro mark for the first time. The results of this report reveal challenges and opportunities for the EU as it seeks to improve its technology capabilities and reinvigorate its industrial base in the context of increasing global competition pressure and ongoing green and digital transformations. |
| Keywords: | industrial R&D |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc132035&r=tid |
| By: | Alexander M. Petersen; Felber Arroyave; Fabio Pammolli |
| Abstract: | A recent analysis of scientific publication and patent citation networks by Park et al. (Nature, 2023) suggests that publications and patents are becoming less disruptive over time. Here we show that the reported decrease in disruptiveness is an artifact of systematic shifts in the structure of citation networks unrelated to innovation system capacity. Instead, the decline is attributable to 'citation inflation', an unavoidable characteristic of real citation networks that manifests as a systematic time-dependent bias and renders cross-temporal analysis challenging. One driver of citation inflation is the ever-increasing lengths of reference lists over time, which in turn increases the density of links in citation networks, and causes the disruption index to converge to 0. A second driver is attributable to shifts in the construction of reference lists, which is increasingly impacted by self-citations that increase in the rate of triadic closure in citation networks, and thus confounds efforts to measure disruption, which is itself a measure of triadic closure. Combined, these two systematic shifts render the disruption index temporally biased, and unsuitable for cross-temporal analysis. The impact of this systematic bias further stymies efforts to correlate disruption to other measures that are also time-dependent, such as team size and citation counts. In order to demonstrate this fundamental measurement problem, we present three complementary lines of critique (deductive, empirical and computational modeling), and also make available an ensemble of synthetic citation networks that can be used to test alternative citation-based indices for systematic bias. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.01949&r=tid |