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The heterogeneous dynamics of economic complexity
Matthieu Cristelli1, Andrea Tacchella1, Luciano Pietronero2
1ISC-CNR, Institute for Complex Systems, Rome, Italy; Physics Department, Sapienza University of Rome, Rome, Italy.
Plos One
|February 12, 2015
Summary
Economic Complexity, a new framework, enhances country competitiveness prediction by analyzing intangible assets versus GDP per capita. It reveals distinct predictable (laminar) and unpredictable (chaotic) growth patterns for nations.
Area of Science:
- Economic Complexity and Dynamical Systems Theory
- Quantitative Economics and Forecasting
Background:
- Predicting Gross Domestic Product (GDP) growth and national competitiveness (e.g., China, US, Vietnam) is crucial for economic policy but remains challenging.
- Traditional economic models struggle with the complex, heterogeneous dynamics observed in national economic evolution.
Purpose of the Study:
- To introduce a novel framework for economic prediction based on the Economic Complexity theory.
- To quantify hidden growth potential using a non-monetary metric for country competitiveness ('fitness') compared to monetary indicators like GDP per capita.
- To develop a data-driven method for assessing future economic evolution with improved predictability.
Main Methods:
- Utilized a recently developed non-monetary metric for country competitiveness ('fitness').
- Analyzed country dynamics on a 'fitness-income plane', comparing intangible asset metrics with GDP per capita.
- Applied concepts from dynamical systems theory, including methods of analogues (Lorenz), to develop a 'selective predictability scheme'.
Main Results:
- Observed strongly heterogeneous patterns in country evolution, with distinct 'laminar' (predictable) and 'chaotic' (unpredictable) regimes.
- Demonstrated that the 'fitness' metric quantifies hidden growth potential, offering new insights beyond traditional economic indicators.
- Showcased the limitations of standard regression techniques in highly heterogeneous economic scenarios.
Conclusions:
- The Economic Complexity framework, incorporating 'fitness', provides a more scientifically robust basis for economic prediction.
- The 'selective predictability scheme' offers a promising data-driven approach for forecasting national economic trajectories, accounting for differing predictability regimes.
- Dynamical systems theory offers essential tools for understanding and predicting complex economic behaviors.
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