Events

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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This paper tests for employer learning about worker ability and quantifies the role of learning in improving the allocation of talent in the labor market for computer scientists. We match the job history of over 40,000 Ph.D. computer scientists (CS) with publications and patent applications that signal their research ability. Workers who publish at CS conferences are twice as likely to move to a top tech firm in the next year as similar coworkers without a publication. Higher-quality papers are often filed as patent applications, but the fact of filing remains private information at the incumbent employer for 18 months. Authors of such papers experience a delayed increase in inter-firm and upward mobility. Without employer learning from public research records, the innovation output of early career computer scientists would drop by 16%. Disclosing patent applications one year faster would increase innovation by 1%, driven by faster positive assortative matching. 

Contact person: Marina Chugunova

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Monetary Incentives for Repeat Interactions: Evidence from an Online Labor Market 
Presenter: Frederike Eulitz (ISTO)
Discussant: Cheng Li (MPI)

Can platform-level changes to monetary incentives increase repeat interactions in online labor markets (OLMs)? We study this question by exploiting a change in the fee structure of the OLM Upwork, which decreased fees if the lifetime billings of a freelancer-buyer relationship surpassed thresholds. We explore the effects of incentive design on repeat interactions, which is theoretically ambiguous in traditional organizational settings, in an OLM. Analyzing a panel dataset of 24,873 freelancers, we reveal that the effects of monetary incentives are not uniform but depend on sub-group characteristics and the unique contextual factors associated with OLMs. Low-earning freelancers respond by increasing repeat interactions, whereas high-earners exhibit a surprising decline. We argue that the impact of monetary incentives depends on platform type. In knowledge-sharing platforms, such incentives may reduce engagement by crowding out intrinsic motivation. However, in platforms with marketplace features, such as OLMs, we argue that monetary incentives induce rational behavior because participants are extrinsically motivated to interact. Our findings contribute to the literatures on platform governance and incentives, offering insights into how platform-level strategies shape participant behavior, and the unintended consequences of platform incentive design.

The Role of Team Composition in Explorative Invention: Analyzing the Impact of Tenure Disparity and Team Size  
Presenter: Jisoo Hur (TUM)
Discussant: Sophia Wetzler (ISTO)

Employee career development is a dynamic process in which individuals adopt different strategies at various stages, leading to variations in their innovative behavior based on organizational tenure. Junior employees, often in the early stages of their careers, bring fresh perspectives and external knowledge, making them valuable contributors to explorative innovation. However, fostering this type of innovation can be challenging when junior inventors collaborate with senior inventors, whose extensive experience in the firm’s technological domain and greater decision-making authority may unintentionally limit juniors’ autonomy and capacity to explore new ideas. This study investigates how tenure differences between junior and senior inventors influence their likelihood of engaging in exploratory inventions. Using patent data to analyze co-inventor relationships, the findings reveal that junior inventors are more likely to engage in exploratory invention when collaborating with peers of similar tenure rather than with senior inventors. Additionally, greater tenure disparity between junior and senior inventors is associated with a decline in exploratory invention. However, larger team sizes help mitigate this negative effect by fostering a more balanced and collaborative environment. These findings provide valuable insights into the dynamics of inventor collaboration and offer practical strategies for organizations to optimize team composition and enhance exploratory innovation.

Scientific Paradigms, Graphics Processing Units and the Evolution of Artificial Intelligence
Presenter: Anna-Sophie Liebender-Luc (MPI)
Discussant: Nicole Wenger-Wong (TUM)

A sudden shift in scientific and technological paradigms lies at the heart of recent advancements in artificial intelligence (AI). Around 2012, traditional symbolic AI gave way to neural networks (NN) as the dominant approach for AI research. This coincided with the sudden successful application of graphics processing units (GPUs) as computational technology. GPUs had been invented for a different application, i.e. accelerating complex graphics displays, mostly in video games. We claim that these developments reflect the nature of breakthrough innovations and have implications for regions competing to become AI leaders. We investigate the role of expertise in GPUs for the uptake of AI innovation across regions globally. To this end, we construct a global database covering 2,088 urban areas for the period from 2000 to 2020. The data encompass a broad set of measures describing AI research and innovation activities, based on publications, patents and startups. We document the ascendancy of neural AI and its association with GPU expertise. Panel OLS and IV regressions demonstrate that after 2012 GPU- and NN-related human capital had a strong effect on the growth of AI-related patents and startups. We discuss implications for innovation policy.

Smallholder farmers excel in informed decision-making through generations of localized knowledge and motivated labor, often achieving higher productivity than large farms. However, the rise of AI-powered precision farming reshapes the agricultural landscape by leveraging big data and advanced algorithms for optimized decision-making. While this innovation promises increased productivity and environmental benefits, it risks marginalizing smallholders due to systemic inequities in data access, intellectual property protections, and agrifood value chain dynamics. The EU agricultural data space initiative aims to democratize farming data, empowering farmers with data control. Yet, its design overlooks the qualitative expertise of smallholders, necessitating inclusive policies to ensure equitable benefits.

This research investigates the role of parental influence in the underrepresentation of women among inventors, despite a growing number of women graduating in STEM fields. Using Danish registry data on individuals born between 1966 and 1985 and an experimental design based on siblings' gender composition, we find that inventorship is less likely to be transmitted from parents to daughters if they have a younger brother (compared to a sister). We replicate these findings with Swedish registry data on individuals born between 1974 and 1988. In a second step, using the Swedish data, we examine and compare the career paths of potential female and male inventors who did not enter the inventive profession. Initial results reveal distinct patterns in the types of professions pursued by female and male non-inventors. Additionally, we observe gendered wage gaps between inventors and non-inventors. Our study offers insights into the factors that influence who becomes an inventor and explores the career outcomes of those who, despite having characteristics associated with inventorship, do not enter the inventive profession.

Contact person: Daehyun Kim

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This seminar will provide an overview of recent theory of the firm research in the setting of interorganizational collaborations.  We will emphasize some of the key parameters of alliance governance and design, and we will revisit some of the classic theoretical work in organizational economics on hybrid governance.  This will be followed by a presentation of an empirical project examining understudied facets of alliance contracting, using new theories to consider how firms might create and capture value in interorganizational relationships.

Contact person: Daehyun Kim

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If firms do less scientific research, and yet their innovation increasingly relies upon science, how do they gain access to scientific knowledge? To explore the role of interpersonal networks among corporate inventors and academic scientists in facilitating the transfer of scientific knowledge from academia to industry, we construct the collaboration network spanning all authors in PubMed and all inventors on U.S.patents. To isolate the influence of interpersonal networks from the inherent characteristics and commercial potential of scientific discoveries, we use paper twins − scientific papers with the same or nearly identical findings published around the same time by different academic teams − and analyze their citations in corporate patents. Although academic science is traditionally viewed as a public good, our findings underscore the critical role of interpersonal relationships in harnessing academic science for corporate innovation. Importantly, the ability of corporate inventors to leverage their interpersonal connections to academic scientists is fully contingent on their own active involvement in both scientific research and commercial technology  development, particularly when this scientific research closely aligns with the academic insights they use for industrial applications

Contact person: Daehyun Kim

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Moderation: Klaus Wiedemann

To eliminate AI content risks, the Chinese government has embraced a zero-risk policy, culminating in a sweeping list of prohibited outputs outlined in Article 4 of its Interim Measures. Jiawei critiques this approach, placing large language models (LLMs) within the broader information ecosystem and exposing fundamental flaws in the policy design. First, the pursuit of fully de-risking LLMs is unattainable. LLMs merely replicate the problems that have already existed in the information marketplace. As the whole information ecosystem is far from perfect, expecting LLMs to function as a zero-risk engine is unrealistic. Additionally, Jiawei argues that the Chinese zero-risk approach is unnecessary. An AI chatbot’s output competes not only with other chatbots but also with other information outlets. Market forces, therefore, have great potential to incentivize AI companies to improve their output and derisk their systems automatically, voluntarily, and continuously. Jiawei uses Article 4 of Chinese Interim Measures as a case study to further illustrate how to design a scientific framework to govern AI risks and avoid setting unrealistic standards and imposing onerous duties on AI companies.