Events

How does industrial policy affect technological change? To analyze this question, we focus on a period of unprecedented state intervention: the economic system of Nazi Germany. After 1933, the government not only tightened and introduced market restrictions, such as foreign exchange and domestic price controls, but also subsidized private investment in autarky and armament industries on a large scale. While previous research has focused on estimating aggregate investment in these industries and the extent to which private firms were forced to invest by the state, this paper analyzes the impact of these policies on R&D activities and, consequently, on technological change. To analyze the direction of technological change, we use a newly constructed patent dataset that includes a sample of over 80,200 patents filed between 1928 and 1941. We use these data to analyze three main research questions. First, did the promotion of investment in autarky industries (e.g. fuel production or ore mining) and armament industries increase patenting in the technology classes associated with these industries? Second, do we observe an increase in the importance of state-owned enterprises or military institutions for R&D? Third, do we find evidence of a change in the direction of R&D activities within large firms? 

Contact person: Michael Rose

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We return to the theoretical foundations of absorptive capacity and test the idea that personal experience in a field makes it easier for firms’ inventors to recognize and build upon local knowledge spillovers from other firms in that field. We propose a new empirical model of localized knowledge diffusion, which 1) measures a firm’s absorptive capacity by its inventors’ prior experience in a field, 2) uses a death instrument to exogenously vary the availability of knowledge of the same collaborative patent in different regions, and 3)estimates the difference in citation likelihood from all subsequent inventors across both regions, as a function of a potentially citing inventor’s prior experience in the field. Consistent with the original theory, firms whose inventors have prior experience in a field are more likely to use locally available spillovers from other firms. No such localization occurs for within firm knowledge diffusion. The effects are stronger for collaborative inventors and more recent knowledge.  (with Benjamin Balsmeier and Sonja Lück)

Contact person: Marina Chugunova

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Medical devices are critical to the delivery of health care, but malfunctioning products can pose a threat to patients, necessitating product recalls. In addition to the direct effects of medical device recalls, such as public health protection and negative reputational and financial consequences for the recalling firm, there may be spillover effects on product development. In particular, the vacuum left by the recalled product and its reputational damage may incentivize competing firms to introduce new medical devices to the market. This study examines the impact of serious medical device recalls on subsequent new product development by competitors, as measured by new medical device submissions to the FDA. We compile data from three regulatory databases representing 11,724 new product submissions and 2,647 recalls for 7,208 unique medical device firms over a 17-year period. Using a fixed effect model and institutional features of the FDA’s clearance process, we find that serious recalls increase other firms’ new product submissions in the affected product market. We estimate that a single recall in a product market increases subsequent submissions by approximately eight percent. Moreover, we find that this relationship is attenuated in markets with more competitors. Our results indicate that firms may currently undervalue actions to avoid product recalls, and that the medical device market may be improved by regulatory efforts to enhance transparency in the recall process.

Contact person: Elisabeth Hofmeister

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Firms are known to provide information to policymakers, but little is known about the conditions under which that information is used in shaping public policy. We theorize that self-interested organizations can strategically use information that is socially constructed as disinterested—scientific knowledge—to shape the language of policies. We also explore how companies’ degree of self-interest and social ties affect policymakers’ willingness to use such information. Focusing on corporate political activity in the form of voluntary filings of amicus briefs submitted to the U.S. Supreme Court, we analyze patent-related cases that the Court considered between 2000 and 2015. We find that firms that cite social science articles in their briefs are more likely to have their arguments reflected in the Court’s opinions. This relationship is positively moderated when the firm’s lawyer has prior clerkship experience at the Supreme Court, highlighting the role of social ties in tailoring briefs to the Court. However, the degree of self-interest negatively moderates the positive impact of scientific information, suggesting that credibility is compromised when firms appear overly vested in the Court ruling.  (with John P. Walsh)

Contact person: Elisabeth Hofmeister

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Recent advances in artificial intelligence (AI) technology demonstrate considerable potential to complement human capital intensive activities. While an emerging literature documents wide-ranging productivity effects of AI, relatively little attention has been paid to how AI might change the nature of work itself. How do individuals, especially those in the knowledge economy, adjust how they work when they start using AI? Using the setting of open source software, we study individual level effects that AI has on task allocation. We exploit a natural experiment arising from the deployment of GitHub Copilot, a generative AI code completion tool for software developers. Leveraging millions of work activities over a two year period, we use a program eligibility threshold to investigate the impact of AI technology on the task allocation of software developers within a quasi-experimental regression discontinuity design. We find that having access to Copilot induces such individuals to shift task allocation towards their core work of coding activities and away from non-core project management activities. We identify two underlying mechanisms driving this shift - an increase in autonomous rather than collaborative work, and an increase in exploration activities rather than exploitation. The main effects are greater for individuals with relatively lower ability. Overall, our estimates point towards a large potential for AI to transform work processes and to potentially flatten organizational hierarchies in the knowledge economy.

Contact person: Cheng Li

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This study examines whether access to generative AI (GenAI) technologies affects entrepreneurial entry and, if so, how. We propose two mechanisms for a potential positive effect: (1) an augmentation channel that pulls prospective entrepreneurs into opportunity-driven entrepreneurship as they automate various peripheral tasks, and (2) an automation channel that pushes displaced wage workers into necessity-driven entrepreneurship as firms automate their core tasks. Leveraging the sudden release of ChatGPT, which democratized public GenAI access, we exploit industry variation in GenAI exposure for the science, technology, engineering, and mathematics (STEM) workforce in a difference-in-differences design. We find that GenAI access leads to increased incorporated entrepreneurship for individuals with higher GenAI exposure. Mechanism tests support the augmentation channel and reveal important heterogeneities in who benefits more from GenAI.
 

Contact person: Daehyun Kim

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This paper analyzes M&A patterns of R&D projects in the antidiabetics industry. For this purpose, we construct a database with all corporate individual antidiabetics R&D projects over the period 1997–2017 and add detailed information on firms’ technology dimension using patent information, next to their position in product markets. This allows us to identify the identity of targets and acquirers (who), the timing of acquisitions along the R&D process (when), and which type of R&D projects changes hands in terms of technology novelty (what). The main results can be summarized as follows. First, most of the action in M&As is in early R&D stages, still far from product markets. Second, most of the early-stage projects that change hands are high-risk/high-gain novel projects. Third, the industry leaders in the product markets are rather inactive in acquiring those novel early-stage projects. The likely acquirers of such projects are small or pipeline firms. Our results put into perspective the narrative that large incumbents acquire small targets with low-risk projects close to product launch. (joint work with J. Malek, J. Seldeslachts and R. Veugelers)

Contact person: Elisabeth Hofmeister

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This event will present the first comprehensive evaluation framework for generative AI models (COMPL-AI), developed by researchers at ETH Zurich, INSAIT, and LatticeFlow AI to bridge the gap between the EU AI Act’s regulatory requirements and technical realities. Mark Vero, a project collaborator and co-author of the paper, will discuss how the COMPL-AI framework translates the AI Act’s principles and requirements into concrete, measurable technical standards, with a focus on large language models (LLMs). Mark will also present the results of evaluating 12 state-of-the-art LLMs, highlighting their shortcomings, particularly in robustness, safety, diversity, and fairness. The session will explore the challenges and opportunities of aligning AI regulation with technical implementation, contributing to the EU’s broader efforts, including the drafting of the General Purpose AI Code of Practice.

Full paper: COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

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About the speaker
Co-author Mark Vero is working on his PhD at the Secure and Reliable Intelligent Systems Lab (SRI Lab) under the supervision of Prof. Martin Vechev at ETH Zurich. His research concerns the privacy and security of LLMs, with a focus on uncovering safety risks in user-facing applications of LLMs. His work has been highlighted at top conferences and workshops in spotlight and oral presentations, has won the Privacy Papers for Policymakers award, and has been featured in international popular media. Prior to his PhD, he completed his Master’s degree with distinction at ETH Zurich in electrical engineering.

Initiative Max Planck Law Tech & Society

The crises of democracy in and across different societies can be articulated as crises of public spaces. In any theory of democracy, be it electoral, liberal, radical, or otherwise, the public spaces feature prominently as one of the core ingredients for democratic societies. In fact, most constitutions create, require, and/or protect public spaces in one way or another. Our expectations for public spaces are correspondingly high. We expect them to foster and form human relationships, offer everyone equal opportunities to participate, and structure and facilitate public debates, while being safe, activating, and inspiring. This begs the question: Do we expect too much?

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The humanet3 project has been established as a joint research group by the Max Planck Institute for Comparative Public Law and International Law, the Max Planck Institute for Innovation and Competition, and the Max Planck Institute for Human Development. Its Principal Investigators are Prof. Armin von Bogdandy, Prof. Josef Drexl and Prof. Iyad Rahwan. The research group is led by Erik Tuchtfeld. It receives central funding from the Max Planck Society for the period from 2023 to 2026.

Following the Covid-19 pandemic, member states of the World Health Organization (WHO) are currently negotiating a Pandemic Preparedness Agreement to prepare the world for future pandemics. The draft agreement includes a mechanism for pathogen access and benefit-sharing under Article 12, which requires users of pathogens with pandemic potential, such as vaccine manufacturers, to provide fixed percentages of vaccines, therapeutics, and diagnostics to the WHO in case of a pandemic as well as annual payments.

One key challenge in the negotiations is whether access to sequence data should be limited as an enforcement mechanism for this mechanism. However, there is no research on how de-coupled approaches to benefit-sharing, that is, approaches that do not limit access to pathogen material and data, could be used to monitor compliance. Our research investigates the sequence data disclosed in patent applications as an indicator for benefit-sharing and compliance monitoring.

Using bioinformatics tools, we identify the users of nucleotide sequences from pathogens with pandemic potential. Our preliminary data suggests that 98% of patents disclosing nucleotide sequence data from pathogens with pandemic potential relate to vaccines, therapeutics, or diagnostics.  However, these patents often include sequences from various organisms, not exclusively pathogens with pandemic potential under the WHO agreement. This indicates that this potential monitoring mechanism would also need to be harmonized with other international instruments governing access and benefit-sharing such as the Convention on Biological Diversity.

The project is part of a work stream within the NIH-funded consortium Pathogen Data Network led by the Swiss Institute of Bioinformatics and part of a work package of the European Viral Outbreak Response Alliance funded by the European Union's HORIZON program.

Contact person: Peter Slowinski

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