Innovation and Productivity in the past, the present and the future – INNOLONG

Economic growth and productivity have been slowing down in advanced economies for decades, raising concerns given society's expectations of continuous growth. Despite total factor productivity growth in the Eurozone dropping from 3.1% (1950-1990) to 0.65% (1990-2022), there was a surge in patent filings and R&D expenditure during this latter period. This period also saw the rise of transformative technologies and the implementation of policies to promote innovation and align education with infrastructure needs. Yet, the continued productivity slowdown, despite these advancements, brings to mind the "Solow Paradox" from 1987. This research project aims at better understanding this paradox by looking at the past, the present and the future of innovation and productivity.
Past - Historical Footprints of Innovation: Utilizing natural language processing, we will assemble and analyze new datasets from various countries, tracing the lineage of innovation from patent publications, censuses and registers. This retrospective will spotlight key innovators and the intrinsic factors that shaped productivity landscapes over the years while providing the research communities with new, ready-to-use datasets.
Present - Evaluating Contemporary Policy Interventions: Many recent public policies were designed to catalyze innovation. In this part, we will compare various strategies, from direct subsidies and tax credits to the funding of public research with the aim of facilitating knowledge spillovers to the private sector. Our approach will combine empirical analysis of existing policies to compare their efficiency, with the development of a new general equilibrium model to understand their global impacts.
Future - Productivity in the AI Era: As AI and disruptive technologies reshape industries, understanding their potential impact on productivity becomes paramount. This segment will probe into the transformative effects of these technologies, especially their influence on labor dynamics and skill demand, taking inspiration from the literature on skill-bias technological change and modeling on top of it the specific features of AI.