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on Technology and Industrial Dynamics |
| By: | Jingni Zhang; David Popp |
| Abstract: | Electric vehicles (EVs) are crucial for cutting transportation emissions, yet the policy drivers of EV innovation remain underexplored. This study analyzes firm-level panel data on EV and battery patents, covering more than 4, 000 firms across 19 countries from 2010 to 2021, to assess how these policy tools and their interactions in different time horizons influence innovative activity. We test the effects of individual policy instruments that either raise demand for EVs or support the development of EV technologies. Stringent fuel-economy standards, financial incentives, adoption targets, and public R&D investments each significantly increase patenting in EV and battery technologies. Moreover, long-term EV targets amplify the innovative impact of public R&D and standards while diminishing the marginal effect of short-term price signals. The results suggest that governments can accelerate clean automotive innovation by combining long-term adoption commitments with sustained R&D investment or strong performance standards, and by managing these instruments as a coordinated policy portfolio rather than as separate tools. The study contributes cross-country, firm-level evidence that links policy design to the direction of clean technology innovation. |
| Keywords: | electric vehicle, technological innovation, policy horizons |
| JEL: | O31 Q55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12421 |
| By: | Andrea Guccione; Pau Roldan-Blanco |
| Abstract: | Firms' innovation outcomes depend on their ability to attract and retain talented inventors. What market frictions prevent the sorting between firms with high innovation potential and high-productivity inventors? How does this sorting impact aggregate innovation, growth and welfare? We address these questions both empirically and theoretically. Empirically, we show that firms facing strong competition in the product market employ more productive inventors, while less productive inventors tend to be allocated in concentrated industries. Theoretically, we embed a frictional labor market for inventors into an endogenous-growth model of strategic innovation. In line with the data, the model predicts that high-productivity inventors are disproportionately employed in firms that operate in competitive industries. We then use the model to quantify the growth and welfare implications of this inventor sorting. Our results show that matching frictions in the market for inventors impede the allocation of high- productivity inventors to firms with high implementation intensity, and are responsible for a 32% loss in economic growth. Industrial policies that subsidize R&D spending relax these frictions by boosting inventor productivity, helping high-quality inventors reallocate to firms with high implementation incentives. Under optimal subsidies, growth increases as much as 74 basis points, closing most of the gap in missing growth caused by frictions in the market for inventors. |
| Keywords: | innovation, inventors, R&D productivity, search |
| JEL: | L16 J6 O3 O4 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1562 |
| By: | Ernest Miguelez; Michele Pezzoni; Fabiana Visentin; Catalina Martínez; Reinhilde Veugelers; Julio Raffo |
| Abstract: | This paper examines the evolving geography of international technological knowledge diffusion over the last four decades using multiple patent-based indicators. We first review the main mechanisms through which knowledge diffuses across borders—including trade and global value chains, foreign direct investment, skilled migration, global science, and markets for technology—highlighting their complementarities and the role of domestic capabilities. We then provide new empirical evidence based on cross-border patent citations, technological trajectories defined by IPC recombinations, patent-to-science linkages, and international patent families. The results reveal persistent asymmetries, with a small group of advanced economies remaining central knowledge hubs, alongside the rising role of emerging countries, especially China. Science-based technologies diffuse farther and faster, while capability constraints continue to limit integration for many regions. |
| Keywords: | Technological knowledge diffusion, Geography, Patents, Citations, Technological trajectories, Science, Patent families |
| JEL: | O34 O33 F14 F23 R12 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:wip:wpaper:92 |
| By: | Lukas Althoff; Hugo Reichardt |
| Abstract: | Artificial intelligence is changing which tasks workers do and how they do them. Predicting its labor market consequences requires understanding how technical change affects workers’ productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-specific productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI’s impact via augmentation, automation, and a third and new channel — simplification — which captures how technologies change the skills needed to perform tasks. Our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent. AI’s equalizing effect is fully driven by simplification, enabling workers across skill levels to compete for the same jobs. We show that the model’s predictions line up with recent labor market data. |
| Keywords: | artificial intelligence, technology, labor markets, growth, inequality, wages, employment |
| JEL: | J24 J31 O33 J23 E24 D31 I26 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12403 |
| By: | Kuosmanen, Natalia; Kuosmanen, Timo |
| Abstract: | Abstract This brief examines how R&D spillovers are associated with firm-level greenhouse gas emissions in Finnish energy-intensive manufacturing. The results show that R&D spillovers from other firms within the same industry are more strongly associated with lower emissions than the firm’s own R&D activity. This result highlights the role of innovation spillovers and knowledge diffusion in emissions abatement. However, the direction and magnitude of R&D spillovers differ across industries depending on their R&D intensity. In the chemical industry that has high R&D intensity, inter-industry R&D spillovers are associated with lower emissions, whereas in the pulp and paper and basic metals industries, inter-industry R&D spillovers are associated with higher emissions. These results demonstrate that technology spillovers do not automatically lower emissions, but can also contribute to higher emissions. Our findings reveal an important channel of inter-industry R&D spillovers through material flows, highlighting the pivotal role of the chemical industry for the GHG abatement in the pulp and paper production and non-metallic minerals industry. |
| Keywords: | Carbon dioxide emissions, Environmental performance, Green productivity, Sustainability, Technology spillovers |
| JEL: | D24 O33 Q52 Q55 Q56 |
| Date: | 2026–02–23 |
| URL: | https://d.repec.org/n?u=RePEc:rif:briefs:175 |
| By: | Florian Misch; Ben Park; Carlo Pizzinelli; Galen Sher |
| Abstract: | The discussion on Artificial Intelligence (AI) often centers around its impact on productivity, but macroeconomic evidence for Europe remains scarce. Using the Acemoglu (2024) approach we simulate the medium-term impact of AI adoption on total factor productivity for 31 European countries. We compile many scenarios by pooling evidence on which tasks will be automatable in the near term, using reduced-form regressions to predict AI adoption across Europe, and considering relevant regulation that restricts AI use heterogeneously across tasks, occupations and sectors. We find that the medium-term productivity gains for Europe as a whole are likely to be modest, at around 1 percent cumulatively over five years. While economically still moderate, these gains are still larger than estimates by Acemoglu (2024) for the US. They vary widely across scenarios and countries and are substantially larger in countries with higher incomes. Furthermore, we show that national and EU regulations around occupation-level requirements, AI safety, and data privacy combined could reduce Europe’s productivity gains by over 30 percent if AI exposure were 50 percent lower in tasks, occupations and sectors affected by regulation. |
| Keywords: | artificial intelligence, productivity, technology, regulation |
| JEL: | E24 J24 O30 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12401 |
| By: | Ville Korpela (Turku School of Economics, University of Turku, Finland); Eero Mäkynen (Turku School of Economics, University of Turku, Finland) |
| Abstract: | Business dynamism has been slowing globally over the last several decades. In a recent study, Akcigit and Ates (2023) examine the relative importance of different channels behind this development and highlight weakened knowledge diffusion from the technology frontier to followers as a dominant force.1 Their study also suggests that diffusion may weaken endogenously as the technology gap widens and market power accumulates, raising the question of how innovation policy can strengthen diffusion without reducing welfare. In this paper we study leader-to-follower licensing as a policy-relevant diffusion margin, and evaluate licensing subsidies relative to direct R&D subsidies. We develop an endogenous-growth general equilibrium model in which firms compete in prices and invest in R&D; the technology leader endogenously chooses whether to license to the follower, trading off higher static profits against faster follower catch-up through knowledge diffusion. We calibrate the model to Finnish data from 2014–2019. Our first exercise evaluates whether allowing licensing is desirable by shutting down the licensing channel in the calibrated economy. In the Finnish benchmark, shutting down licensing lowers growth but increases consumption-equivalent welfare, because the level effects of reduced concentration dominate the diffusion benefits of licensing. We then vary the diffusion rate through licensing and product substitutability to characterize when licensing becomes welfare-improving. In that region, solving the policymaker’s problem shows a non-trivial interaction: higher R&D subsidies can reduce equilibrium licensing by moving leaders more quickly into the monopoly-pricing states where licensing is privately unattractive, so the optimal policy mix augments R&D support with a non-negligible licensing subsidy to sustain diffusion. |
| Keywords: | Antitrust Policy, Business Dynamism, Endogenous Growth, Innovation Policy, Licensing, Technology Diffusion |
| JEL: | E22 L10 L41 O33 O34 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:tkk:dpaper:dp174 |
| By: | Srikanth Pai ((Corresponding author), Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025, India.); Akila Hariharan (Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025); Naveen Srinivasan (Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025) |
| Abstract: | We build a tractable model that links institutional dynamics with the private value of innovation. Our approach differs from much of the existing literature in that an inventor does not retain a perpetual monopoly over its use, and the cash flows generated from a new idea are uncertain. In our framework the relevant dimension of institutional quality is enforcement strength. We model institutional strength as a two-state continuous-time Markov chain. This makes the cash flows from innovation stochastic and state-dependent, and hence the incentive to innovate varies with the strength of enforcement regime. Countries alternate between periods of strong and weak enforce-ment, reflecting irregular political and legal events such as reforms, leadership changes, or crises. Our model shows how institutional fragility can alter the incentive to innovate and connects institutional dynamics with cross-country differences in standard of living. |
| Keywords: | institutions, innovation, patents, continuous-time Markov chain, economic growth |
| JEL: | O31 O33 O34 O43 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2026-292 |
| By: | Francesco Androni (Gran Sasso Science Institute); Andrea Ascani (Gran Sasso Science Institute); Alberto Marzucchi (Gran Sasso Science Institute) |
| Abstract: | The phenomenon of zombie firms has been increasing through time in the last decades. Prior re search has extensively examined the role of zombie firms in credit misallocation and weak insolvency regimes However, limited attention is paid to how the competitive environment has influenced its surge. The study aims at linking the diffusion of zombie formation with the field of industrial dynam ics. The analysis focuses on whether the intensity of competition influences the diffusion of zombie f irms, by assessing competition forces such as firm entry and innovation intensity. We use micro aggregated data at the region-sector level to analyse the diffusion of zombie firms in Italy for the years from 2014 to 2020, and identify a substantive role of reallocation forces in driving the shares of zombie firms. Competition in the form of entry and, albeit more weakly, innovation intensity reduces the diffusion of zombie firms, ultimately showing that a decrease in competition intensity is part of the phenomenon. This research contributes to understanding the relationship between zombie firms and sluggish economic activity, describing further factors that affect their formation and persistence. |
| Keywords: | Zombie firms; competition; firm entry; innovation; industrial dynamics |
| JEL: | O33 O31 L25 R11 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ahy:wpaper:wp65 |
| By: | Eugenie Dugoua; Joelle Noailly |
| Abstract: | This paper examines the patterns and mechanisms of global clean technology diffusion over the last two decades. We document four stylized facts: uneven sectoral progress favoring power and light transport; China’s dominance in innovation and manufacturing; the role of modularity in driving cost declines; and limited adoption in developing economies. Through case studies of solar, electric vehicles, and hydrogen, we analyze how policy and infrastructure enable scale. Finally, we assess emerging challenges for the next phase of diffusion, including critical mineral constraints, artificial intelligence, and geopolitical fragmentation. |
| Keywords: | Clean technology diffusion, Climate change mitigation, Renewable energy, Industrial policy, Solar photovoltaics, Electric vehicles, Hydrogen |
| JEL: | O33 Q55 O25 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:wip:wpaper:95 |
| By: | Daron Acemoglu; David Autor; Simon Johnson |
| Abstract: | This paper defines pro-worker technologies, including Artificial Intelligence, as technologies that make human skills and expertise more valuable by expanding worker capabilities. Our conceptual framework distinguishes among five categories of technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only the last category is unambiguously pro-worker, generating demand for novel human expertise rather than commodifying it. We illustrate these distinctions through hypothetical and real-world examples spanning aviation maintenance, electrical services, custodial work, education, patent examination, and gig delivery. While AI’s capacity to automate work is substantial, we argue that its potential to serve as a collaborator, by extending human judgment, enabling new tasks, and accelerating skill acquisition, is equally transformative and currently underexploited. We identify market failures, including misaligned firm and developer incentives, path dependence, and a pervasive pro-automation ideology, that may lead to underinvestment in pro-worker AI. We consider nine policy directions that would change incentives, including targeted investments in health care and education, tax code reform, antitrust enforcement, and intellectual property protections for worker expertise. |
| JEL: | J23 J24 J31 M50 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34854 |
| By: | Fabiano Compagnucci (Gran Sasso Science Institute); Mauro Gallegati (Università Politecnica delle Marche); Andrea Gentili (Università Mercatorum); Enzo Valentini (Università di Macerata) |
| Abstract: | This paper investigates the relationship between robotisation—understood as a key driver of innovation—and its impact on employment and household income, with a particular emphasis on the role of firm size at the industry level across regions in the Euro area. In the microeconomic literature, larger firms are generally viewed as more likely to adopt robotisation and more vulnerable to labour saving effects than smaller firms. However, the spatial dimension of this relationship remains underexplored. To address this gap, we calculate the Adjusted Penetration of Robots at the sectoral level by integrating data from the International Federation of Robotics on robot stocks, the EUROSTAT Regional Database, and the Structural Analysis (STAN) database, covering 150 NUTS 2 regions in the Euro area. We then perform a spatial stacked panel analysis incorporating various firm size metrics. Our findings challenge prevailing microeconomic insights. At the regional level, areas with a high prevalence of small firms show a negative correlation between robotisation and household income and employment. In contrast, in regions dominated by non-small firms, robotisation positively correlates with employment but does not result in corresponding increases in household income. These findings indicate that the regional impacts of robotisation may diverge substantially from the aggregated performance of individual firms, as highlighted in the microeconomic literature. |
| Keywords: | Robotisation, Employment, Households’ Income, Firms’ size, Regional Divergence |
| JEL: | O33 J24 D02 R23 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ahy:wpaper:wp69 |
| By: | Guillermo Cruces; Diego Fernández Meijide; Sebastian Galiani; Ramiro H. Gálvez; María Lombardi |
| Abstract: | Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizational selection compresses educational heterogeneity, leaving unclear whether AI narrows productivity gaps across individuals with substantially different levels of formal education. We address this question using a randomized online experiment conducted outside firms, in which 1, 174 adults ages 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our design targets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AI access, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI access, this gap falls to 0.139 standard deviations, implying that generative AI closes about three quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing cognitive constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance. |
| JEL: | J24 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34851 |
| By: | Pinelopi Koujianou Goldberg; Reka Juhasz; Nathan Lane; Giulia Lo Forte; Jeff Thurk; Pinelopi Goldberg |
| Abstract: | The resurgence of subsidies and industrial policies has raised concerns about their potential inefficiency and alignment with multilateral principles. Critics warn that such policies may divert resources to less efficient firms and provoke retaliatory measures from other countries, leading to a wasteful “subsidy race.” However, subsidies for sectors with inherent cross-border externalities can have positive global effects. This paper examines these issues within the semiconductor industry: a key driver of economic growth and innovation with potentially significant learning-by-doing and strategic importance due to its dual-use applications. Our study aims to: (1) document and quantify recent industrial policies in the global semiconductor sector, (2) explore the rationale behind these policies, and (3) evaluate their economic impacts, particularly their cross-border effects, and compatibility with multilateral principles. We employ historical analysis, natural language processing, and a model-based approach to measure government support and its impacts. Our findings indicate that government support has been vital for the industry’s growth, with subsidies being the primary form of support. They also highlight the importance of cross-border technology transfers through FDI, business and research collaborations, and technology licensing. China, despite significant subsidies, does not stand out as an outlier compared to other countries, given its market size. Model estimates suggest the presence of learning-by-doing at the firm-product level as well as economies of scope within a firm and substantial cross-border learning spillovers. These spillovers likely reflect cross-country technology transfers and the role of fabless clients and input suppliers in disseminating knowledge globally through their interactions with foundries. Such cross-border spillovers are not merely accidental but result from deliberate actions by market participants that cannot be taken for granted. Firms may choose to share knowledge across borders or restrict access to frontier technology, thereby excluding certain countries. Future research will use model estimates to simulate the quantitative implications of subsidies and to explore the dynamics of a “subsidy race” in the semiconductor industry. |
| Keywords: | semiconductors, industrial policy, subsidies, learning-by-doing, multilaterism |
| JEL: | F13 F61 L63 N60 O38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12495 |