Events

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?

More information 

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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We analyze the relationship between firms’ ability to leverage regulatory slack and their market entry strategies. Using newly constructed genomic measures of disease market similarity, we systematically document evidence of forum shopping, whereby pharmaceutical firms seek the most lenient regulatory environment for approval when drugs have multiple potential therapeutic uses. Firms seek regulatory approval in smaller disease markets to lower the costs of regulation and rely on complementary, non-regulatory pathways - in the form of unapproved, “off-label” drug use - to expand demand. Our data allow us to characterize the degree to which new technologies can exploit such opportunities, shedding light on how firms navigate regulatory environments to speed the entry of new products to market.

Contact person: Elisabeth Hofmeister

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This research discusses the relationship between employee perceptions of sustainable human resource management and job satisfaction in 54 countries. The authors propose that sustainable HRM is positively associated with job satisfaction but that this relationship is moderated by employees’ identification with the organization and country-level individualism–collectivism. Thus, the authors suggest national culture functions as a second level moderator of the relationship of sustainable HRM with organizational identification on job satisfaction. Findings from the multi-level analyses using data from 14,502 employees nested within 54 countries provided support for our hypotheses, namely that employee perceptions of sustainable HRM were positively associated with job satisfaction and that this relationship was more pronounced for employees with lower levels compared to higher levels of organizational identification in individualistic rather than collectivistic countries. These findings bear important implications for both theory and practice.

This paper develops new procedures to measure environmental performance in cross-country firm-level data, explore the heterogeneity in environmental performance and its relationship with economic performance based on data from Croatia, France, Indonesia, and Lithuania. It documents extensive firm heterogeneity in carbon productivity (defined as value added per tonne of CO2 emitted) within narrowly defined industries, which significantly exceeds the extent of heterogeneity in labour productivity. On average, the 90th percentile firm is 22 times more carbon productive than the 10th percentile firm in the same industry (compared with seven times for labour productivity). This heterogeneity has important implications for aggregate emissions: raising the carbon productivity of the least productive firms to the carbon productivity of the median firm in their industry would reduce carbon emissions by 72% for the same level of output in total across the four countries. Furthermore, a growing carbon productivity dispersion is associated with a lower carbon productivity growth. Industries that are more dispersed in carbon productivity are also more dispersed in labour productivity and firms that are more carbon productive are also more labour productive, even when controlling for other factors. These correlations also hold in changes over time and suggest that structural characteristics of both firms and industries may jointly explain both economic and environmental outcomes. Firm-level regressions show that a plausibly exogenous increase in energy prices – as would be induced by a carbon tax – cause a significant fall in CO2 and an improvement in carbon productivity, without detrimental economic effects. This evidence suggests that there is a significant untapped potential of improved environmental performance and reduction in industrial emissions, and that these improvements could be achieved without affecting economic performance.

Contact person: Albert Roger

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