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Sci Adv. 2019 Jan 9;5(1):eaau1705. doi: 10.1126/sciadv.aau1705. eCollection 2019 Jan.

Interpreting economic complexity.

Mealy P1,2,3, Farmer JD1,2,4,5,6, Teytelboym A1,2,7.

Author information

1
Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford OX2 6ED, UK.
2
Smith School for Enterprise and the Environment, University of Oxford, Oxford OX1 3LP, UK.
3
Bennett Institute for Public Policy, University of Cambridge, Cambridge, CB3 9DT, UK.
4
Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
5
Santa Fe Institute, Santa Fe, NM 87501, USA.
6
Mathematical Institute, University of Oxford, Oxford OX1 3LP, UK.
7
Department of Economics, University of Oxford, Oxford OX1 3UQ, UK.

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.

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