Interpreting economic complexity.

Area of Science:

• Economics
• Network Science
• Data Analysis

Background:

• Economic complexity index (ECI) and product complexity index (PCI) are key metrics for understanding economic development.
• These indices offer insights into global trade and specialization patterns.

Purpose of the Study:

• To demonstrate the equivalence of ECI and PCI to spectral clustering algorithms.
• To explore the relationship between ECI, PCI, and dimensionality reduction techniques.
• To elucidate the empirical success of ECI in explaining economic disparities.

Main Methods:

• Spectral clustering algorithm applied to a similarity graph.
• Analysis of dimensionality reduction methods including diffusion maps and correspondence analysis.
• Examination of export basket diversity and its correlation with economic indicators.

Main Results:

• ECI and PCI are mathematically equivalent to a spectral clustering partitioning a similarity graph.
• ECI and PCI show strong correlations with dimensionality reduction techniques.
• Countries with high ECI specialize in high-PCI products, explaining cross-country GDP per capita and economic growth variations.
• Specialization patterns were identified across U.S. states and U.K. regions.

Conclusions:

• ECI and PCI provide a robust framework for analyzing economic complexity and development.
• The spectral clustering interpretation offers new theoretical insights into these economic measures.
• These indices are valuable tools for understanding regional and national economic specialization.