Events

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.

On 17/18 December 2024, the Max Planck Institute for Innovation and Competition will host the 7^th Research on Innovation, Science and Entrepreneurship Workshop (RISE7), an annual workshop for Ph.D. students and Junior Post-docs in Economics and Management. 

The goal of the RISE7 Workshop is to stimulate an in-depth discussion of a select number of empirical research papers. It offers Ph.D. students and Junior Post-docs an opportunity to present their work and to receive feedback.

Keynote speaker of the RISE7 Workshop is Alexander Oettl (Georgia Tech)

Program RISE7 2024

For more information see RISE Workshop.

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

Subscription to the invitation mailing list and more information on the seminar page.

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

Subscription to the invitation mailing list and more information on the seminar page.

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

Subscription to the invitation mailing list and more information on the seminar page.

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.

In an experimental idea generation context, we study the effect of input incentives, which reward time invested to generate ideas, and output-based incentives, which reward the number of innovative ideas generated, compared to simple fixed wages. Overall, we find that both input and output incentives increase the average number of innovative ideas to a comparable extent. Under input incentives, this increase is due to a rise in effort duration, i.e., an increase in the time spent generating ideas. For output incentives, this increase is due to both heightened effort duration and effort intensity, i.e., the number of ideas generated per unit of time. The optimal incentive scheme seems to depend on the context. We find that output incentives result in innovative ideas at lower costs. However, output incentives also cause a shift in the types of ideas generated, with individuals focusing on less time-intensive ideas. (Joint work with Kay Blaufus and Kevin Piehl)

Contact person: Svenja Friess

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The common practice to include firm fixed effects in empirical research may eliminate the explanatory power of important economic factors that are persistent. To illustrate this point, we review the intuitive R&D-patent relation in recent studies and present a surprising pattern that R&D input only positively explains patent output in half of prior regression estimations. This “missing link” can be attributed to the persistence of R&D and patents that causes the between-firm variation to be absorbed by firm fixed effects. We consider modified Hausman-Taylor estimates and advanced machine learning methods, and find that both methods lead to a consistent positive R&D-patent relation. In particular, the advanced machine learning methods select some firm dummies that have explanatory power for patent output, and exclude other firm dummies are not informative for – and may even bias – the identification. This paper thus offers two methods to serve as a “second opinion” for empirical researchers working with explanatory variables that strongly correlate with between-individual unobservables.

Contact person: Daehyun Kim

Subscription to the invitation mailing list and more information on the seminar page.