Veranstaltungen

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.

Ansprechpartner: Daehyun Kim

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

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.

Ansprechpartner: Daehyun Kim

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

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.

Ansprechpartner: Daehyun Kim

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

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)

Ansprechpartnerin: Svenja Friess

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

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.

Ansprechpartner: Daehyun Kim

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

We use a production function framework to estimate how government-funded R&D leads to the creation of new scientists and science. Focusing on public investments into specific energy technologies by the US Department of Energy (DOE), we leverage quasi-experimental variation in R&D investment that emerges when Congress appropriates more or less funding than what the DOE requests to estimate the effect of public funding on dissertation flows and recover the marginal costs of producing new (potential) scientists as proxied by dissertation flows. We also document the relationship between dissertations (potential scientists) and patents (new science). Our use of the production function framework allows us to recover technology-specific productivity trends and estimate the number of scientists and amount of science that would have been produced under alternative funding allocations. Given how noise in the budgeting process is prevalent, our adaptation of the production function framework could prove useful in many other settings.

Ansprechpartnerin: Ulrike Morgalla

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.