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on Innovation |
| By: | Kumar Rishabh (University of Basel); Roxana Mihet (Swiss Finance Institute - HEC Lausanne); Julian Jang-Jaccard (Swiss Federal Office for Defence Procurement) |
| Abstract: | Does AI make firms vulnerable or resilient to cyber risk? To answer this, we develop a novel measure identifying AI-intensive U.S. public firms using publicly available patents and business-description data. While cyber threats typically suppress innovation, AI-intensive firms neutralize this effect. This protective effect strengthens with greater AI experience. Moreover, firms combining AI innovation and implementation exhibit a stronger buffer protecting their innovation and financial outcomes under cyber stress, whereas firms merely implementing AI without internal innovation gain no such resilience. Our results emphasize internal AI innovation as fundamental in enabling firms to effectively withstand cyber threats. |
| Keywords: | Cyberrisk, artificial intelligence, innovation, resilience, economics of AI, economics of cybercrime |
| JEL: | D8 O3 O4 G3 L1 L2 M1 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2539 |
| By: | Ashish Arora; Sharon Belenzon; Jungkyu Suh; Hansen Zhang |
| Abstract: | We study the post-World War II “Golden Age” of American corporate research from 1945 to 1980, using multiple indicators of corporate research activity. We use an ensemble learning approach to classify firms as either Science Leaders, Absorbers or Followers. Our analysis reveals that only a small fraction of firms, whom we call Leaders, invest in internal research that is on the scientific frontier, with the objective to generate breakthrough inventions. Absorbers invest in research principally to absorb external scientific discoveries to fuel their inventive activity. Followers typically generate incremental innovations, using older scientific knowledge. Consistent with this, we find Leaders were more likely to be at the technological frontier, enjoy greater market power, and benefit from government procurement contracts. As universities and startups began to commercialize academic discoveries, the need for “absorptive corporate labs” declined. The shift ultimately transformed the American innovation landscape, deepening the division of innovative labor between universities, startups, and incumbent corporations, with only a select group of Leader firms continuing to invest in basic science. |
| JEL: | O3 O31 O33 O34 O35 O36 O38 O39 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33713 |
| By: | Joshua S. Gans |
| Abstract: | This paper examines how the introduction of artificial intelligence (AI), particularly generative and large language models capable of interpolating precisely between known data points, reshapes scientists' incentives for pursuing novel versus incremental research. Extending the theoretical framework of Carnehl and Schneider (2025), we analyse how decision-makers leverage AI to improve precision within well-defined knowledge domains. We identify conditions under which the availability of AI tools encourages scientists to choose more socially valuable, highly novel research projects, contrasting sharply with traditional patterns of incremental knowledge growth. Our model demonstrates a critical complementarity: scientists strategically align their research novelty choices to maximise the domain where AI can reliably inform decision-making. This dynamic fundamentally transforms the evolution of scientific knowledge, leading either to systematic “stepping stone” expansions or endogenous research cycles of strategic knowledge deepening. We discuss the broader implications for science policy, highlighting how sufficiently capable AI tools could mitigate traditional inefficiencies in scientific innovation, aligning private research incentives closely with the social optimum. |
| JEL: | D82 O30 O34 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33566 |
| By: | Pierre Azoulay; Daniel P. Gross; Bhaven N. Sampat |
| Abstract: | The U.S. government has funded university research for nearly 80 years, with a significant share of this funding supporting the fixed costs of science through indirect cost recovery (ICR). We explain the history, objectives, and mechanics of ICR policy and review key controversies. We also provide new empirical evidence on indirect costs at the NIH, a major target of past and present ICR reform. Using data from over 350 institutions, we find that while negotiated ICR rates average 58%, effective rates—what NIH actually pays—average 42%, with relatively little variation across institutions or over time. Our analyses also suggest that a proposed 15% flat rate would significantly cut NIH funding for many grantees, disproportionately affecting institutions most linked to commercial patenting and drug development. We conclude by assessing the current system, and major reform proposals, across several ICR policy objectives: support for research and infrastructure, cost-efficiency incentives, implementation costs, and transparency. No single approach dominates on all dimensions. |
| JEL: | H51 O31 O32 O38 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33627 |
| By: | Matano, Alessia (University of Barcelona); Naticchioni, Paolo (Roma Tre University) |
| Abstract: | This paper investigates the relationship between China’s import competition and the innovation strategies of domestic firms. Using firm level data from Italy spanning 2005-2010 and employing IV fixed effects estimation techniques, we find that the impact of China’s import competition on innovation varies depending on the type of goods imported (intermediate vs. final). Specifically, imports of final goods boost both product and process innovation, while imports of intermediate goods reduce both. Additionally, we extend the analysis to consider the role of unions in moderating these responses. We find that, in unionized firms, imports' impact on innovation is mitigated, specifically to protect workers' employment prospects. |
| Keywords: | unions, product and process innovation, final and intermediate goods, China’s import competition, IV fixed effects estimations |
| JEL: | C33 L25 F14 F60 O30 J50 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17764 |
| By: | Ashish Arora; Sharique Hasan; William D. Miles |
| Abstract: | Solving complex problems- in medicine, engineering, and other technological domains- often requires exploring multiple approaches, particularly when significant uncertainty exists about which one will lead to success. Conventional wisdom assumes that having many experimenters independently decide which approaches to pursue increases diversity and, thus, also the chances of finding a solution. However, if experimenters herd toward the most promising approach, this convergence may reduce diversity and thus the likelihood of solving the problem. In this paper, we develop a simple model to show that, holding the total number of experiments constant, markets dominated by a few large-scale experimenters- firms conducting multiple experiments- explore more diverse approaches than markets with many single-shot experimenters. Single-shot experimenters tend to converge on the most promising approach, while multi-experimenters are more likely to diversify to avoid the correlation inherent in pursuing multiple experiments within the same approach. We test our model's predictions using data from pharmaceutical R&D. Our analysis shows that increasing the average number of experiments per firm by one unit raises target diversity by over three standard deviations. In turn, a one-standard deviation increase from the mean in target diversity boosts the likelihood of at least one experiment reaching Phase 1 clinical trials by 25.9 percentage points. Our findings inform policies for the optimal allocation of experiments across firms to maximize approach diversity and market-level success. |
| JEL: | L1 L2 O31 O32 O33 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33682 |
| By: | Arntz, Melanie (ZEW Mannheim); Böhm, Michael Johannes (TU Dortmund); Graetz, Georg (Uppsala University); Gregory, Terry (LISER); Lehmer, Florian (Institute for Employment Research (IAB), Nuremberg); Lipowski, Cäcilia (ZEW) |
| Abstract: | We investigate the diffusion of frontier technologies across German firms before and during the Covid-19 crisis. Our analysis tracks the nature, timing, and pandemic-related motivations behind technology investments, using tailor-made longitudinal survey data linked to administrative worker--firm records. Technologies adopted after the onset of the pandemic increasingly facilitated remote work and mitigated the negative employment effects of the crisis. Overall, however, investments in frontier technologies declined sharply, equivalent to a loss of 1.4 years of pre-pandemic investment activity. This procyclical adoption pattern is particularly striking since the pandemic created clear incentives to experiment with new technologies. Our findings highlight how short-run fluctuations may influence medium-run economic growth through their impact on technology diffusion. |
| Keywords: | cyclicality of technology adoption, firm-level survey data, frontier technology investments, Covid-19 crisis |
| JEL: | O33 E22 E32 J23 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17846 |