• DOI:10.1038/s42254-020-00275-1
• Corpus ID: 233441596

Economic complexity theory and applications

@article{Hidalgo2021EconomicCT,
  title={Economic complexity theory and applications},
  author={C{\'e}sar A. Hidalgo},
  journal={Nature Reviews Physics},
  year={2021},
  volume={3},
  pages={92 - 113}
}

• César A. Hidalgo
• Published 25 January 2021
• Economics
• Nature Reviews Physics

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. Economic complexity methods predict changes in the geography of economic activities and explain differences in economic growth, inequality, greenhouse emissions and labour market outcomes. This Review summarizes a decade of research on economic complexity and its applications. 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. 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.