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Project

Scientific and technological novelty: new measurement approaches and impact assessment

Lack of risk-taking in science and technology research is weighing on our capacity to develop novel technological breakthroughs. Technological breakthroughs are essential to boost productivity and tackle grand societal challenges like climate change (e.g., decarbonisation of heavy industry, electricity storage, or large-scale deployment of carbon capture and storage technologies). Novel technologies have higher potential for major impact, but are also subject to higher uncertainty. Novel technologies take longer to reach their impact, either due to technological prematurity or because they require major system changes, which prompt resistance from incumbent infrastructures. The high-risk/high-gain nature of novel technological research makes it particularly appropriate for public support. The objective of this Phd project is to evaluate how to foster high-risk/high-gain research with the potential to generate novel breakthroughs. The project proposes three lines of research. The first contributes to a recent but growing literature in economics of science and innovation centered on the development of new indicators and methodologies to assess the nature of technological novelty using machine learning and natural language processing tools. The second aims to evaluate empirically distinct science and technology policy approaches that can better promote high-risk/high-gain scientifc and technological research. The third focuses on the assessment of the economic and social impacts of novel technological breakthroughs, as well as their effects on firms’ market structure. The project will make use of matched PATSTAT and ORBIS data (potentially complemented with SCOPUS data), and explore the viability of new techniques for causal inference such as semantic fixed effects and semantic matching.

Date:25 Mar 2019 →  25 Mar 2023
Keywords:Novelty, Economics of innovation, Science and technology policy, Natural language processing, Econometrics
Disciplines:Innovation and technology management, Innovation, research and development, technological change, intellectual property rights, Natural language processing
Project type:PhD project