Interpreting economic complexity

New research by Alex Teytelboym et al. presented at the 2018 Big Data Conference was recently published in Science Advances. The article, “Interpreting Economic Complexity,” explores patterns related the economic complexity index (ECI) and product complexity index (PCI). The paper can be found here.

 

Abstract: Two network measures known as the economic complexity index (ECI) and product complexity index (PCI) have provided important insights into patterns of economic development. We show that the ECI and PCI are equivalent to a spectral clustering algorithm that partitions a similarity graph into two parts. The measures are also closely related to various dimensionality reduction methods, such as diffusion maps and correspondence analysis. Our results shed new light on the ECI’s empirical success in explaining cross-country differences in gross domestic product per capita and economic growth, which is often linked to the diversity of country export baskets. In fact, countries with high (low) ECI tend to specialize in high-PCI (low-PCI) products. We also find that the ECI and PCI uncover specialization patterns across U.S. states and U.K. regions.