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on Innovation |
| By: | Serguey Braguinsky; Joonkyu Choi; Yuheng Ding; Karam Jo; Seula Kim |
| Abstract: | We provide evidence that mega firms have played an increasingly important role in shaping new technological trajectories in recent years. While the share of novel patents---defined as patents introducing new combinations of technological components---produced by mega firms declined until around 2000, it has rebounded sharply since then. Furthermore, we find that the technological impact and knowledge diffusion of novel patents by mega firms have grown relative to those by non-mega firms after 2001. We also explore potential drivers of this trend, presenting evidence that the rise in novel patenting by mega firms is tied to their disproportionate increase in cash holdings and the expansion of their technological scope. Our findings highlight an overlooked positive role of mega firms in the economywide innovation process. |
| Keywords: | Mega Firms; Innovation; Novel Patents; Knowledge Diffusion |
| JEL: | O31 O33 O34 L11 L25 |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-60 |
| By: | Luisa Corrado; Stefano Grassi; Aldo Paolillo (Cambridge Judge Business School, University of Cambridge) |
| Abstract: | Recent studies suggest that space activities generate significant economic benefits. This paper attempts to quantify these effects by modelling both business cycle and long-run effects driven by space sector activities. We develop a model in which technologies are shaped by both a dedicated R&D sector and spillovers from space-sector innovations. Using U.S. data from the 1960s to the present day, we analyse patent grants to distinguish between space and core sector technologies. By leveraging the network of patent citations, we further examine the evolving dependence between space and core technologies over time. Our findings highlight the positive impact of the aerospace sector on technological innovation and economic growth, particularly during the 1960s and 1970s. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:jbs:wpaper:202502 |
| By: | Lapo Santarlasci; Armando Rungi; Antonio Zinilli |
| Abstract: | This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02287 |
| By: | Sipeng Zeng; Xiaoning Wang; Tianshu Sun |
| Abstract: | Although Artificial Intelligence (AI) holds great promise for enhancing innovation and productivity, many firms struggle to realize its benefits. We investigate why some firms and industries succeed with AI while others do not, focusing on the degree to which an industrial domain is technologically integrated with AI, which we term "domain AI readiness". Using panel data on Chinese listed firms from 2016 to 2022, we examine how the interaction between firm-level AI capabilities and domain AI readiness affects firm performance. We create novel constructs from patent data and measure the domain AI readiness of a specific domain by analyzing the co-occurrence of four-digit International Patent Classification (IPC4) codes related to AI with the specific domain across all patents in that domain. Our findings reveal a strong complementarity: AI capabilities yield greater productivity and innovation gains when deployed in domains with higher AI readiness, whereas benefits are limited in domains that are technologically unprepared or already obsolete. These results remain robust when using local AI policy initiatives as instrumental variables. Further analysis shows that this complementarity is driven by external advances in domain-AI integration, rather than firms' own strategic pivots. Time-series analysis of IPC4 co-occurrence patterns further suggests that improvements in domain AI readiness stem primarily from the academic advancements of AI in specific domains. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09634 |
| By: | Brandão, Tiago |
| Abstract: | This paper offers a critical historical analysis of the intellectual and institutional precursors to the open innovation paradigm. Challenging the perception of open innovation as a radical departure from earlier models, the paper demonstrates that many of its core principles—such as external collaboration, absorptive capacity, and distributed knowledge flows—have deep roots in 20th-century innovation practices and theories. Through an extensive review of foundational literature in innovation studies, strategic management, and organizational learning, this extended paper traces how ideas of inter-firm cooperation, technological brokering, and institutional embeddedness shaped current open innovation frameworks. Emphasis is placed on the path-dependent nature of absorptive capacity, the strategic management of complementary assets, and the evolution of innovation networks. By revisiting contributions from Mowery, Teece, Cohen and Levinthal, March, Hagedoorn, Powell, and others, the study repositions open innovation within a broader intellectual trajectory, offering a more nuanced understanding of its origins and limitations. |
| Date: | 2025–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:2nbs3_v1 |
| By: | Philipp Hohn; Torben Klarl |
| Abstract: | This paper suggests a micro-founded growth theory of human capital that incorporates three important ingredients: i) learning in a knowledge network, ii) possible skill-down-grading due to knowledge obsolescence, and, iii) fear of technological unemployment due to automation. Heterogeneous agents (optimally) split their time between learning-by-exchanging knowledge or working in the final goods sector. On the aggregate level, our benchmark model shows that learning and the degree of connectivity within the knowledge network directly impact the growth rate of the economy. Moreover, we show the existence of a poverty trap in which society stagnates due to an insufficient level of human capital that is in particular governed by the degree of knowledge obsolescence. In an extension, we control for the fact that learning is a cognitively demanding task associated with learning errors due to cognitive constraints. Therefore, two groups of agents are distinguished: Cognitively constrained and rational optimizers, where both can switch endogenously between a low and high-skilled state. We use this extension to numerically quantify the effects of cognitive constraints on human capital inequality. Inter alia, we show that a knowledge obsolescence shock has transitional as well as long-run negative effects on human capital inequality, where in relative terms, cognitively constrained agents are more affected than their rational counterparts. |
| Keywords: | Human capital, innovation, inequality, automation, knowledge network |
| JEL: | O11 O33 O40 E23 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:atv:wpaper:2503 |
| By: | Stefan Buechele (University of Kassel, INCHER and Institute of Economics); Guido Buenstorf (University of Kassel, INCHER and Institute of Economics); Matthias Huegel (University of Kassel, INCHER and Institute of Economics); Johannes Koenig (University of Kassel, INCHER and Institute of Economics; IAB Institute for Employment Research, Saarbruecken); Maria Theissen (University of Kassel, INCHER and Institute of Economics) |
| Abstract: | Previous research has shown that a large fraction of tenured university faculty in the U.S. and other countries were trained at a small number of highly prestigious universities. The question remains whether this concentration is due to competitive advantages held by candidates from these universities, or whether it merely reflects the larger output of early-career researchers aspiring to faculty positions by these universities. To address this question, we analyze data covering the full population of doctoral graduates in Germany since the 1960s. Similar to studies of the U.S. system of higher education, we observe a strong concentration of professors trained at only a small number of universities. However, we find no evidence indicating that the prestige of the doctoral degree-granting university systematically affects individuals’ odds of being appointed to professorships. Despite increasing stratification tendencies, our results do not indicate that the importance of the degree-granting university for academic careers has increased. While doctoral graduates from top-tier universities are more likely to secure faculty positions at similar institutions, this is mostly due to returns to their own alma mater after initial appointments elsewhere. |
| Keywords: | Faculty appointment, university prestige, stratification, academic labor market, professorship, Germany, Habilitation |
| JEL: | I24 J24 J40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:mar:magkse:202516 |
| By: | Askitas, Nikos (IZA) |
| Abstract: | Generative AI (GenAI) and Large Language Models (LLMs) are moving into domains once seen as uniquely human: reasoning, synthesis, abstraction, and rhetoric. Addressed to labor economists and informed readers, this paper clarifies what is truly new about LLMs, what is not, and why it matters. Using an analogy to auto-regressive models from economics, we explain their stochastic nature, whose fluency is often mistaken for agency. We place LLMs in the longer history of human–machine outsourcing, from digestion to cognition, and examine disruptive effects on white-collar labor, institutions, and epistemic norms. Risks emerge when synthetic content becomes both product and input, creating feedback loops that erode originality and reliability. Grounding the discussion in conceptual clarity over hype, we argue that while GenAI may substitute for some labor, statistical limits will, probably but not without major disruption, preserve a key role for human judgment. The question is not only how these tools are used, but which tasks we relinquish and how we reallocate expertise in a new division of cognitive labor. |
| Keywords: | automation and outsourcing, technological change, labor economics, autoregressive models, Large Language Models, Generative Artificial Intelligence, human-machine collaboration knowledge work, epistemic norms, digital transformation |
| JEL: | J24 O33 O31 J22 D83 L86 J44 O38 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18070 |
| By: | Grant Fleming; Frank Liu; David Merrett; Simon Ville |
| Abstract: | We analyse prize winners at international exhibitions held in Australia in the second half of the nineteenth century as a means of estimating the significance of female inventors. It provides an alternative measure from the more common focus on patents. While both methodologies – patents and exhibitions – have shortcomings, exhibitions appear to be more inclusive of sectors in which female inventiveness was concentrated including apparel, textiles and the creative arts. This is confirmed by the larger share of women inventors as exhibition prize-winners compared with patentees. Female patentees and prize-winners possessed similar characteristics – many were married, lived locally, and were occasional and individualist in their creative habits. |
| Keywords: | Female inventors; Australia; international exhibitions; prize winners; creative industries; patenting |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:auu:hpaper:133 |
| By: | Enrique Ide |
| Abstract: | Recent advances in Artificial Intelligence (AI) have fueled predictions of unprecedented productivity growth. Yet, by enabling senior workers to perform more tasks on their own, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential skills. In this paper, I develop a model to examine how advanced automation affects the intergenerational transmission of knowledge. The analysis reveals that automating entry-level tasks yields immediate productivity gains but can undermine long-run growth by eroding the skills of subsequent generations. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce U.S. long-term annual growth by approximately 0.05 to 0.35 percentage points, depending on its scale. I also demonstrate that AI co-pilots - systems that democratize access to expertise previously acquired only through hands-on experience - can partially mitigate these negative effects. However, their introduction is not always beneficial: by providing expert insights, co-pilots may inadvertently diminish younger workers' incentives to invest in hands-on learning. These findings cast doubt on the optimistic view that AI will automatically lead to sustained productivity growth, unless it either generates new entry-level roles or significantly boosts the economy's underlying innovation rate. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16078 |
| By: | Arenal Alberto (Universidad Politecnica de Madrid); Armuna Cristina (Universidad Politecnica de Madrid); Feijoo Claudio (Universidad Politecnica de Madrid); Ramos Sergio (Universidad Nacional de Educacion a Distancia); Xu Zimu (Cranfield University Cranfield School of Management); Moreno Ana Maria (Universidad Politecnica de Madrid) |
| Abstract: | Beyond the mainstream discussion on the key role of China in the global AI landscape, the knowledge about the real performance and future perspectives of the AI ecosystem in China is still limited. This paper evaluates the status and prospects of China's AI innovation ecosystem by developing a Triple Helix framework particularized for this case. Based on an in-depth qualitative study and on interviews with experts, the analysis section summarizes the way in which the AI innovation ecosystem in China is being built, which are the key features of the three spheres of the Triple Helix -governments, industry and academic/research institutions-as well as the dynamic context of the ecosystem through the identification of main aspects related to the flows of skills, knowledge and funding and the interactions among them. Using this approach, the discussion section illustrates the specificities of the AI innovation ecosystem in China, its strengths and its gaps, and which are its prospects. Overall, this revisited ecosystem approach permits the authors to address the complexity of emerging environments of innovation to draw meaningful conclusions which are not possible with mere observation. The results show how a favourable context, the broad adoption rate and the competition for talent and capital among regional-specialized clusters are boosting the advance of AI in China, mainly in the business to customer arena. Finally, the paper highlights the challenges ahead in the current implementation of the ecosystem that will largely determine the potential global leadership of China in this domain. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16526 |
| By: | Torben Klarl |
| Abstract: | This paper investigates the impact of creativity on technological advancement, long-term economic development, and social welfare, with creativity endogenously determined through interactions within social networks. We demonstrate that an economy remains stagnant, exhibiting neither networking nor long-term growth, when the size of the creative class falls below a certain positive threshold. Conversely, surpassing this threshold triggers active networking between creative and non-creative individuals, fostering sustained technological progress and income growth. We calibrate the model and simulate the economy’s transition from stagnation to dynamic growth. Although immediate welfare gains from transitioning to a growing economy are modest, medium- to long-term welfare improvements become substantial due to the cumulative effects of technological advancement facilitated by networking. |
| Keywords: | Creativity, Population dynamics, Innovation, Technological evolution, Endogenous growth, Network, Welfare |
| JEL: | E13 E14 I30 O11 O31 O33 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:atv:wpaper:2504 |