The Building Blocks of Economic Complexity

TL;DR

This paper introduces a network-based measure of economic complexity derived from trade data, showing its strong correlation with income levels and predictive power for future growth, emphasizing the importance of complexity in development.

Contribution

It develops a novel network approach to quantify economic complexity and demonstrates its relevance for understanding and predicting economic development.

Findings

Complexity measures correlate with income levels.

Deviations predict future economic growth.

Complexity influences development trajectories.

Abstract

For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them. Here we develop a view of economic growth and development that gives a central role to the complexity of a country's economy by interpreting trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country's economy by characterizing the structure of this network. Furthermore, we show that the measures of complexity we derive are correlated with a country's level of income, and that deviations from this relationship are predictive of future growth. This suggests that countries tend to converge to the level of income dictated by the complexity of their productive structures, indicating that development efforts should focus on generating the conditions that would allow complexity to emerge in order to generate sustained growth and prosperity.