Veranstaltungen

Mehr Information auf der SIPLA Webseite

We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. In particular, a simple change to the first step of the estimation procedure provides a first-order bias correction for the GMM estimator in the second step. Furthermore, a modification of the moment condition in the second step ensures Neyman orthogonality and enhances efficiency and robustness by rendering the asymptotic distribution of the GMM estimator invariant to estimation noise from the first step.

Ansprechpartner: Jordan Bisset

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

The workshop will cover the MSI’s three focus areas:

• Digitalization, Strategy and Organization
• Innovation and Entrepreneurship
• Law & Economics of Intellectual Property, Innovation & Digitalization

Like the Munich Summer Institute, the MSI Ph.D. Workshop will focus on quantitative empirical research. In the workshop, participants will present their working papers, receive comments from senior scholars, and discuss their papers with other participants. The number of participants is limited. Discussants will be senior scholars who participate in the Munich Summer Institute’s main conference.

Program

Das Munich Summer Institute (MSI) wird veranstaltet von Center for Law & Economics der ETH Zürich, der Universität Lausanne, der Cornell University, dem Imperial College, dem Lehrstuhl für Technologie- und Innovationsmanagement der TUM, dem Lehrstuhl für Innovationsökonomie der TUM, dem Institut für Strategie, Technologie und Organisation (ISTO) der LMU München und dem Max-Planck-Institut für Innovation und Wettbewerb.

Weitere Informationen auf der Webseite des MSI

This paper studies the relationship between climate policy, market power and innovation. Using data on patenting and firms' balance sheets, I document four stylized facts. Most importantly, I find that firms with a higher degree of market power are, on average, more invested in dirty technologies than their direct competitors. Motivated by the empirical evidence, I develop a model of directed technical change with strategic innovation incentives. A carbon tax affects market power within industries due to technology lock-in and firm heterogeneity. Strategic incentives lead some firms to respond to climate policy by increasing their dirty innovation investments. In the calibrated model, a carbon tax lowers aggregate markups and increases clean innovation along the green transition, highlighting the importance of firms' strategic decisions for climate policy.

Ansprechpartner: Fernando Loaiza

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

This paper studies how a major advance in artificial intelligence reshapes the production of scientific knowledge. I exploit the public release of AlphaFold2—a deep-learning system that predicts protein structures with near-experimental accuracy—as a sharp and field-wide reduction in the cost of structural information in structural biology. I construct a new protein-level dataset linking the universe of proteins to their experimental structural characterization, AlphaFold2 coverage, and the complete corpus of associated scientific publications. Following the release, proteins receiving larger informational shocks experience substantial increases in scientific activity: the probability of publication rises by 60–80 percent, and publication counts more than double relative to pre-release trends. These gains are not uniform. Publication responses are strongest for proteins that had partial, but incomplete, experimental structural information prior to AlphaFold2, indicating increasing returns to existing scientific capital. Rather than displacing experimentation, AlphaFold2 complements wet-lab research: experimental validation activity increases for proteins with high predicted coverage. The composition and organization of research teams also shift. Projects on high-coverage proteins involve more specialized and computationally oriented contributors, and resulting publications engage more intensively with structural and computational questions while maintaining experimental inquiry.

Ansprechpartner: Michael Rose

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Zum Programm 

Zu REGIS: Reading Group in Innovation Studies

Titel und Abstract folgen.

Ansprechpartner: Jordan Bisset

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.

Titel und Abstract folgen.

Ansprechpartner: Elisabeth Hofmeister

Eintragung in den Einladungsverteiler und mehr Informationen auf der Seminarseite.