Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies.
This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution.
The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies. This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution. The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

In dieser Dissertation werden zwei Hauptprobleme der Kommunikation in der experimentellen Ökonomie anhand einer detaillierten Analyse von einem Piratenspiel und einem Öffentliche-Güter-Spiel Experiment diskutiert: Vergleichbarkeit und Messung von Kommunikation. Um eindeutige Ergebnisse aus verschiedenen Kommunikationsexperimenten ziehen zu können, muss Kommunikation zunächst gründlicher klassifiziert werden. Die Dissertation schlägt Dimensionen vor, die für eine solche Klassifizierung auf theoretischer Basis verwendet werden können. Zweitens wird empirisch gezeigt, wie maschinelles Lernen die Verhaltensökonomie voranbringen kann, indem es Informationen aus Kommunikationsdaten extrahiert und Folgeverhalten vorhersagt. Mögliche Implikationen dieser Technologie für Politik, Wirtschaft und Gesellschaft im Allgemeinen werden skizziert. Da diese Implikationen derzeit noch weitgehend unerforscht sind, empfiehlt die Dissertation letztlich, dass die Verhaltensökonomie an dieser Entwicklung partizipiert und nicht einfach darauf wartet, dass sie eintritt.

Am 7. und 8. Dezember 2015 fand am Leibniz-Institut für Wirtschaftsforschung Halle (IWH) zum 16. Mal der IWH-CIREQ Macroeconometric Workshop statt. Die in Kooperation mit dem Centre interuniversitaire de recherche en économie quantitative (CIREQ), Montréal, durchgeführte Veranstaltung beschäftigte sich dieses Mal mit zentralen Herausforderungen, denen sich die ökonomische Prognose zu stellen hat: Strukturbrüche in den Daten, statistische Revisionen und Fehler bei der Messung wichtiger Indikatoren.