  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 related to various dimensionality reduction methods and can
be interpreted as vectors that determine distances between nodes based on their
similarity. Our results shed a new light on the ECI's empirical success in
explaining cross-country differences in GDP/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 (low) PCI products. We also find
that the ECI and PCI uncover economically informative specialization patterns
across US states and UK regions.
