Events

Moderation: Dr. Natale Rampazzo

Max Planck Institute for Innovation and Competition, Munich, Room 313

Abstract:
Generic penetration in the U.S. pharmaceutical market has increased, providing significant gains in consumer surplus. What impact has this had on the rate and direction of pharmaceutical innovation? While the overall level of drug development activity has increased, our estimates suggest a sizable, robust, negative relationship between rising generic penetration and early-stage pharmaceutical innovation in the same therapeutic areas. We also find that increasing generic penetration induces firms to shift their R&D activity towards more biologic-based products and away from chemical-based products. We conclude by discussing potential implications of our results for long-run welfare, policy, and innovation.

Contact Person: Zhaoxin Pu

Max Planck Institute for Innovation and Competition, Munich, Room 313

Abstract:
We study the impact of foreign direct investment (FDI) on total factor productivity (TFP) of domestic firms using a new, representative firm-level data set spanning six countries. A novel finding is that firm-level spillovers from foreign firms to domestic companies can be significantly positive, non-existent, or even negative, depending on which sectors receive FDI. When foreign firms produce in the same narrow sector as domestic firms, the latter are negatively affected by increasing competition and positively affected by knowledge spillovers. We find that the positive spillovers dominate if foreign firms enter sectors where firms are “technologically close,” controlling for the endogeneity of their entry decision into such sectors. Positive technology spillovers also affect firms in other sectors, if those sectors are technologically close to the sectors receiving FDI. Increasing FDI in sectors that are technologically close to other sectors boosts TFP of domestic firms by twice as much as increasing FDI by the same amount across all sectors.

Contact Person: Zhaoxin Pu

Max Planck Institute for Innovation and Competition, Room E10

Max Planck Institute for Innovation and Competition, Munich, Room 313

This talk will review the speaker’s and other researchers’ efforts to quantify technological change. Some challenges have been at least partially met but others are still outstanding. The important issues include what to measure (the dependent variable) and a variety of economic and technical measures will be considered with the conclusion that functional performance metrics are the most informative about what we want to learn. To quantify change, we also need to decide what the performance metrics theoretically depend upon (the independent variable). One obvious candidate is time but given work by Wright and many others, the presentation will also consider whether an effort variable such as cumulative demand/production or R&D spending improves the understanding of technological change. After making contestable decisions on the variables, the result for a wide variety of technological domains appears to be a generalization of Moore’s Law. However, this exponential relationship with time is quite noisy but more importantly, many (probably most) researchers of technological change do not find the generalized Moore’s Law (GML) acceptable. The final part of the presentation will be discussion and speculation about various reasons for this reality including practical utility, quantitative theoretical foundations and deep qualitative reasoning.

 
Contact Person: Dr. Marco Kleine

Moderation: Luc Desaunettes

Max Planck Institute for Innovation and Competition, Munich, Room 313

This talk will review the speaker’s and other researchers’ efforts to quantify technological change. Some challenges have been at least partially met but others are still outstanding. The important issues include what to measure (the dependent variable) and a variety of economic and technical measures will be considered with the conclusion that functional performance metrics are the most informative about what we want to learn. To quantify change, we also need to decide what the performance metrics theoretically depend upon (the independent variable). One obvious candidate is time but given work by Wright and many others, the presentation will also consider whether an effort variable such as cumulative demand/production or R&D spending improves the understanding of technological change. After making contestable decisions on the variables, the result for a wide variety of technological domains appears to be a generalization of Moore’s Law. However, this exponential relationship with time is quite noisy but more importantly, many (probably most) researchers of technological change do not find the generalized Moore’s Law (GML) acceptable. The final part of the presentation will be discussion and speculation about various reasons for this reality including practical utility, quantitative theoretical foundations and deep qualitative reasoning.

 
Contact Person: Dr. Fabian Gaessler

Max Planck Institute for Innovation and Competition, Munich, Room 313

It has been hypothesized by von Hippel and von Krogh (2016) that problem solving often occurs via the simultaneous recognition of both a need and a responsive solution – without prior formulation of a problem being required. If this hypothesis is correct, significant new opportunities are opened up for both research and practice. The absence of a requirement for problem formulation can significantly reduce the effort and complexity of problem-solving. It also eliminates constraints on the range of possible solutions that a problem statement inevitably imposes, and so may enable the discovery of more creative, novel, and valuable solutions. In this talk, I will give an introduction to the phenomenon of need-solution pairs and then report on a first test of the von Hippel and von Krogh hypothesis that we conducted via a laboratory experiment. In summary, we find that need-solution-pairs can be triggered in everyday life situations and that both the novelty and creativity of solutions discovered via need-solution pair recognition are significantly higher than solutions discovered via the traditionally assumed need-first pattern. I will conclude by demonstrating the practical implications of this new phenomenon and our experimental research.

 
Contact Person: Felix Poege

Moderation: Dennis Kann

Max Planck Institute for Innovation and Competition, Munich, Room 313

 
Contact Person: Zhaoxin Pu