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on Innovation |
| By: | Emanuele Bazzichi; Massimo Riccaboni; Fulvio Castellacci |
| Abstract: | We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02189 |
| By: | Alejandro Bello-Pintado (Universidad Pública de Navarra); Carlos Bianchi (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Sofía Maio (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | This study examines how innovation modes, STI (Science and Technology-based Innovation), DUI (Innovation based on learning-by-Doing, learning-by-Using, learning by-Interacting) and their combination, shape firms’ use of formal and informal intellectual property protection mechanisms (IPPM) and influence product innovation performance. Using panel data from the National Innovation Activities Survey (2010 2021) of Uruguay, results show that STI drives formal IPPM and enhances innovation likelihood and novelty, while DUI fosters informal IPPM with limited impact on innovation outcomes. However, combined STI-DUI strategies generate coordination tensions, constraining innovation performance |
| Keywords: | innovation modes, knowledge appropriability strategies, firm organization |
| JEL: | O31 O32 O54 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-02-26 |
| By: | Balazs Egert |
| Abstract: | This paper reviews the contributions of the 2025 Nobel Prize in Economics laureates, Joel Mokyr, Philippe Aghion and Peter Howitt, to our understanding of innovation-driven economic growth, situating their work within the broader evolution of modern growth theory and empirical evidence. It highlights why the Industrial Revolution marked a transition to sustained, self-reinforcing technological progress and shows how Mokyr's emphasis on knowledge, culture and institutions complements Aghion and Howitt's Schumpeterian framework, which formalises innovation as a competitive process of firm entry, exit and technological replacement. The paper then uses these frameworks to interpret the widespread productivity slowdown observed in advanced OECD economies since the mid-2000s, arguing that weakened creative destruction, slower diffusion of frontier technologies, declining business dynamism and policy headwinds are key explanatory factors. |
| Keywords: | innovation, productivity, economic growth, creative destruction, institutions |
| JEL: | O30 O40 O43 L16 N10 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12572 |
| By: | Susan Helper; Resem Makan; Daniel W. Shoag |
| Abstract: | We study the establishment of U.S. National Laboratories in the 1940s–1950s to estimate local spillovers from public research infrastructure. This setting allows us to causally identify such spillovers, for two reasons: 1) Lab sites were chosen largely for security and political reasons, rather than existing or potential innovative capability and 2) We identify runner-up locations using archival sources. We find several types of knowledge spillovers: Compared to control counties, Lab counties experience large and persistent increases in patenting by non-lab inventors; non-lab patents in the same county shift toward laboratories’ research fields and cite laboratory patents more frequently. Using newly digitized county data from 1936–1970, we find sustained increases in retail sales and household income. Linked 1940–1950 Census records show wage gains for pre-existing residents who remain in lab counties, with larger effects for college-educated workers. We find that cohorts exposed to laboratory establishment during school-age years attained more education, consistent with a human-capital channel. Spillovers arise despite extensive secrecy around early nuclear research, suggesting that co-location with public R&D can generate sizable local benefits even under restricted information flows. |
| JEL: | O31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35011 |
| By: | Moh Hosseinioun; Brian Uzzi; Henrik Barslund Fosse |
| Abstract: | Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.27956 |
| By: | Christian Rutzer; Dragan Filimonovic; Jeffrey T. Macher; Rolf Weder |
| Abstract: | The CD index is a widely used measure of disruptive inventions. Most studies compute it using USPTO data. This creates a puzzle because the US appears less disruptive than European and Asian countries. We show that this largely stems from missing international citations. Using a global citation network, we quantify and correct this bias. The disruptiveness advantage of non-US inventors drops by 64% to 148% of the US baseline mean. The US emerges as a disruption leader over Europe, with Asia's advantage substantially reduced. Globally integrated citation data are essential for credible measurement of disruptive innovation in international contexts. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.17881 |
| By: | Nigar Hashimzade; Haoran Sun |
| Abstract: | Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.29542 |
| By: | Anton Korinek; Joseph E. Stiglitz |
| Abstract: | Rapid progress in new technologies such as AI has led to widespread anxiety about adverse labor market impacts. This paper asks how to guide innovative efforts so as to increase labor demand and create better-paying jobs while also evaluating the limitations of such an approach. We develop a theoretical framework to identify the properties that make an innovation desirable from the perspective of workers, including its technological complementarity to labor, the relative income of the affected workers, and the factor share of labor in producing the goods involved. Applications include robot taxation, factor-augmenting progress, and task automation. In our framework, the welfare benefits of steering technology are greater the less efficient social safety nets are. As technological progress devalues labor, the welfare benefits of steering are at first increased but, but beyond a critical threshold, decline and optimal policy shifts toward greater redistribution. Moreover, as labor's economic value diminishes, steering progress focuses increasingly on enhancing human well-being rather than labor productivity. |
| JEL: | D63 E64 O3 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34994 |
| By: | Adam B. Jaffe; Laura B. Shupp; Valentina Tartari |
| Abstract: | This Chapter surveys the findings of social science research on the contribution of universities to innovation and economic growth, both locally/regionally and globally. In the last several decades research has demonstrated universities’ causal effects through the mechanisms of knowledge creation, education and training of students, and technology transfer/entrepreneurship. The Chapter summarizes how the literature has studied each of these mechanisms, and how the findings have probed variation across disciplines and economic sectors. The depth and breadth of understanding have been advanced by new microdata and new methods of linking data across inventions, scientists and institutions, and by application of methods from network science. We emphasize that research has proven the importance of these effects on average, but to date has less to say about the determinants of success or failure in different contexts. These findings have implications for public policy to foster innovation both regionally and globally. |
| JEL: | I2 O3 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35017 |
| By: | Werner, Sven; Sievert, Maximiliane; Haney, Aoife Brophy; Trotter, Philipp |
| Abstract: | Start-ups are an important component of driving context-sensitive sustainable development in emerging markets based on domestic innovation. However, knowledge on how best to support the capabilities, networks and access to finance of such ventures is limited, specifically in emerging markets. In this paper, we leverage novel data from a pan-African start-up accelerator to understand whether and why accelerators are effective. Adopting an entrepreneurial ecosystem lens and conceptualizing accelerators as intermediaries within ecosystems, we test two competing views of accelerator effectiveness: substitution and complementarity. Our results provide support for a complementarity view, where the positive effects of accelerators are higher in more mature ecosystems. We contribute to the literature by drawing attention to the importance of the context within which accelerators are situated, challenging the predominant approach of substituting for missing ecosystem components in emerging markets. |
| Abstract: | Start-ups gelten als wichtiger Motor für eine kontextsensitive und nachhaltige Entwicklung in Ländern mit niedrigen und mittleren Einkommen, insbesondere wenn sie auf lokalen Innovationen beruht. Dennoch ist bislang nur begrenzt bekannt, wie sich die Fähigkeiten, Netzwerke und der Zugang zu Finanzmitteln dieser Unternehmen effektiv fördern lassen - insbesondere im Kontext von Ländern mit niedrigen und mittleren Einkommen. In diesem Beitrag nutzen wir neuartige Daten eines panafrikanischen Start-up-Accelerators, um zu untersuchen, ob und warum Accelerator-Programme wirksam sind. Aufbauend auf der Perspektive unternehmerischer Ökosysteme und der Konzeption von Accelerators als Intermediäre innerhalb dieser Ökosysteme testen wir zwei konkurrierende Erklärungsansätze für ihre Wirksamkeit: Substitution und Komplementarität. Unsere Ergebnisse stützen die Komplementaritätsperspektive, wonach die positiven Effekte von Accelerators in reiferen Ökosystemen stärker ausgeprägt sind. Damit leisten wir einen Beitrag zur Literatur, indem wir die Bedeutung des institutionellen und ökosystemischen Kontexts, in den Accelerators eingebettet sind, hervorheben. Wir hinterfragen den verbreiteten Ansatz, fehlende Ökosystemkomponenten in Schwellenländern durch isolierte Fördermaßnahmen ersetzen zu wollen. |
| Keywords: | Accelerator, start-up, entrepreneurial ecosystem, entrepreneurship support, impact assessment |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:339617 |
| By: | Leyan Wu; Yong Huang; Wei Lu; Akrati Saxena; Vincent Traag |
| Abstract: | Scientific knowledge flows enable cumulative progress by connecting researchers across disciplines, institutions, and countries. Yet it remains unclear how geography and national structures continue to shape these exchanges in an increasingly connected world. Using a large-scale bibliometric dataset from OpenAlex, which covers 39.35 million publications across 95 countries and 3, 794 cities between 2000 and 2022, we examine global knowledge diffusion through two complementary channels: co-authorship and citation. We find that the constraining effect of geographic distance on collaboration has not diminished over time but has instead intensified, suggesting persistent structural or institutional barriers. Citation flows, by contrast, are less sensitive to spatial proximity, indicating that intellectual influence may diffuse more freely across borders. At the country level, research networks exhibit strong domestic preferences and a shared citation orientation toward the United States. China, while increasingly favored as a collaboration partner by other countries, continues to be systematically undercited within global citation flows. International mobility increases researchers' collaboration with scholars in their host country but has limited effects on citation flows. These results highlight the structural persistence of spatial and country biases in global science, with implications for equitable participation and recognition across regions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.01602 |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, and AIAS, Aarhus University); Schroede, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Örebro University School of Business) |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures. |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2026_003 |
| By: | Donna K. Ginther; Joshua L. Rosenbloom |
| Abstract: | This chapter examines the dramatic growth and evolving role of postdoctoral researchers in the U.S. scientific workforce from 1979 to 2023, highlighting a fourfold increase in postdoc numbers that outpaced growth in graduate students and faculty. We argue that this expansion reflects the fragmented nature of science funding, particularly the effects of the NIH budget doubling in the early 2000s, which increased both supply and demand for postdocs but ultimately worsened employment conditions. The chapter also explores the career outcomes of postdocs, noting limited economic returns outside academia and declining transitions to faculty roles. With recent declines in postdoc numbers, tightening immigration policies, and rising compensation, it seems likely that the U.S. may have reached “peak postdoc, ” potentially leading to reduced future research output. The chapter concludes with a call for improved data and further research to better understand postdocs’ roles in scientific production and career development. |
| JEL: | I23 J40 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35014 |
| By: | Alexander Bick; Adam Blandin; David Deming; Nicola Fuchs-Schündeln; Jonas Jessen |
| Abstract: | AI adoption is much higher among American workers than it is among European workers. Is this widening the gap between U.S. and EU productivity growth? |
| Keywords: | generative artificial intelligence (AI); technology adoption; labor productivity |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:102954 |
| By: | Andrew Johnston; Christos A. Makridis |
| Abstract: | Does artificial intelligence (AI) increase productivity - and does it displace workers? We examine aggregate effects using administrative data covering essentially all U.S. employers in a difference-in-differences design exploiting occupational AI exposure across industries and states. A one standard deviation increase in exposure raises output by 7%, with effects emerging in 2021 when enterprise AI tools entered the market. Employment effects follow the same timing but diverge by exposure type: where AI likely requires human collaboration, employment rises 4%; where AI can perform tasks independently, we find no significant employment effect. Results are robust to state-by-year and industry-by-year fixed effects and suggest AI has caused a decrease in the labor share of income. |
| Keywords: | artificial intelligence, generative AI, aggregate productivity, labor market, technological change |
| JEL: | O33 J24 J23 E24 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12579 |