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Economic complexity theory and applications

Nature Reviews Physics volume 3, pages 92–113 (2021)Cite this article

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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.

Keywords

  • Economic complexity involves the use of machine learning and network techniques to predict and explain the economic trajectories of countries, cities and regions.

  • Measures of relatedness — which estimate the affinity between economies and activities — anticipate changes in specialization patterns and explain labour market outcomes, such as income loss and unemployment.

  • Economic complexity measures are reduced-dimensionality representations of specialization matrices that explain the geography of hundreds of economic activities.

  • Measures of economic complexity explain and predict international and regional variations in income, economic growth, income inequality, gender inequality and greenhouse emissions.

  • Economic complexity methods have been validated by studies at multiple geographic scales (from countries to cities) and a variety of economic activities (products, industries, occupations, patents, research papers).

  • Relatedness metrics can be unpacked into multiple channels (such as industry, occupation and location-specific knowledge) to understand the drivers of regional diversification.

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Fig. 1: Relatedness is constructed using networks that connect similar activities.
Fig. 2: Economic complexity.
Fig. 3: Economic complexity index for regions in Brazil, China, Japan, Canada, Spain and Russia.
Fig. 4: Economic complexity compared with measures of concentration, population and income.
Fig. 5: Examples of the principle of relatedness in action.
Fig. 6: Summary of literature on economic complexity and relatedness.

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Acknowledgements

The author thanks Pierre-Alexandre Balland, Cristian Jara-Figueroa and Mary Kaltenberg for their useful comments. This work was supported by ANITI (ANR-19-PI3A-0004).

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  1. Center for Collective Learning, Artificial and Natural Intelligence Toulouse Institute (ANITI), University of Toulouse, Toulouse, France

    César A. Hidalgo

  2. Alliance Manchester Business School, University of Manchester, Manchester, UK

    César A. Hidalgo

  3. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    César A. Hidalgo

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César A. Hidalgo is a founder and CEO of Datawheel, a company specialized in the creation of data distribution and visualization systems, including the Observatory of Economic Complexity, Pantheon.World, DataUSA.io and DataMéxico, among other platforms.

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Hidalgo, C.A. Economic complexity theory and applications. Nat Rev Phys 3, 92–113 (2021). https://doi.org/10.1038/s42254-020-00275-1

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