Economic Complexity Theory and Applications
Abstract
Economic complexity methods have become popular tools in economic geography, international development, and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions, and income inequality.
About the Speaker
Cesar A. Hidalgo directs the Center for Collective Learning at the Artificial and Natural Intelligence Institute (ANITI, University of Toulouse). Prior to joining ANITI, he directed the Collective Learning group at MIT. Hidalgo holds a PhD in Physics from the University of Notre Dame and is the author of dozens of peer-reviewed papers and three books. His latest book is How Humans Judge Machines (MIT Press, 2021).