Economic Complexity: why we like "Complexity weighted diversification"
Luciano Pietronero, Andrea Gabrielli, and Andrea Zaccaria

TL;DR
This paper discusses the importance of Complexity-weighted diversification in predicting economic growth, clarifies misconceptions about the ECI algorithm, and highlights the advantages of the Fitness approach for forecasting economic dynamics.
Contribution
It clarifies that the ECI algorithm does not include diversification, emphasizes the robustness of the Fitness approach, and introduces quantitative trajectories in GDP per capita and Fitness space for economic forecasting.
Findings
Complexity-weighted diversification predicts country growth effectively.
The ECI algorithm contains no diversification, explaining its poor performance.
The Fitness approach enables quantitative forecasting of economic trajectories.
Abstract
A recent paper by Hausmann and collaborators (1) reaches the important conclusion that Complexity-weighted diversification is the essential element to predict country growth. We like this result because Complexity-weighted diversification is precisely the first equation of the Fitness algorithm that we introduced in 2012 (2,3). However, contrary to what is claimed in (1), it is incorrect to say that diversification is contained also in the ECI algorithm (4). We discuss the origin of this misunderstanding and show that the ECI algorithm contains exactly zero diversification. This is actually one of the reasons for the poor performances of ECI which leads to completely unrealistic results, as for instance, the derivation that Qatar or Saudi Arabia are industrially more competitive than China (5,6). Another important element of our new approach is the representation of the economic…
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Taxonomy
TopicsEconomic and Technological Innovation
