Words to Numbers

Organizational Economics | Academic Year 2022/2023

Extracting Information from Unstructured Text

When studying economic questions empirically, we often need to find numeric measures for constructs such as culture, trust, individual traits, strength of institutions, etc. However, most human interaction is not numeric, but verbal. Only recently, computer-based analytical methods have become available to study large amounts of text and speech data, in astringent, more objective way. This computer-linguistic revolution has great impact for all the social sciences, and economics in particular, because we now can apply these methods to distill complex concepts such as "trust" down to numbers.

Spokesperson

Prof. Dr. Florian Englmaier
Prof. Dr. Florian Englmaier

LMU Munich

Organizational Economics

Members

Prof. Elliott Ash, Ph.D.

ETH Zürich

Law, Economics and Data Science

Prof. Diego Battiston, Ph.D.

University of Edinburgh

Economics

Prof. Dr. Alexia Delfino, Ph.D.

Università Bocconi

Economics

Prof. Miguel Andres Espinosa Farfan, Ph.D.

Bocconi University

Economics

Prof. Dr. Jana Gallus

UCLA Anderson

Behavioral Economics

Prof. Ricard Gil, Ph.D.

Queen’s University

Business Economics

Prof. Nicola Lacetera, Ph.D.

University of Toronto

Economics

Prof. Raffaella Sadun, Ph.D.

Harvard Business School

Economics

Dr. Caspar Siegert

Bank of England

Economics

Prof. Erina Ytsma, Ph.D.

Carnegie Mellon

Economics

Events