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A machine learning approach to economic complexity based on matrix completion.

Giorgio Gnecco1, Federico Nutarelli2, Massimo Riccaboni2

  • 1AXES Research Unit, IMT School for Advanced Studies, 55100, Lucca, Italy. giorgio.gnecco@imtlucca.it.

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Summary
This summary is machine-generated.

Matrix Completion (MC) enhances economic complexity analysis by reconstructing Revealed Comparative Advantage (RCA) matrices. This method yields a novel index (MONEY) that better predicts country-product advantages, indicating higher economic complexity.

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Area of Science:

  • Economics
  • Computer Science
  • Machine Learning

Background:

  • Economic complexity is typically analyzed using trade flow data to create Revealed Comparative Advantage (RCA) matrices.
  • Existing economic complexity indices often rely on a limited number of eigenvectors derived from RCA data.
  • Machine learning methods, particularly Matrix Completion (MC), have potential for analyzing complex economic datasets.

Purpose of the Study:

  • To apply Matrix Completion (MC) techniques to reconstruct and analyze Revealed Comparative Advantage (RCA) matrices for economic complexity assessment.
  • To introduce a novel economic complexity index, MONEY (Matrix cOmpletion iNdex of Economic complexitY), based on MC.
  • To compare the performance of MC-based methods against state-of-the-art economic complexity indices.

Main Methods:

  • Utilized Matrix Completion (MC), a machine learning approach, to reconstruct incomplete RCA matrices from yearly trade flow data.
  • Developed a high-accuracy binary classifier based on MC to distinguish RCA values above or below one.
  • Introduced the MONEY index, derived from MC, measuring the predictability of RCA entries and reflecting economic complexity.

Main Results:

  • MC successfully reconstructed RCA matrices with high accuracy.
  • The MC-based binary classifier demonstrated superior performance compared to previous machine learning applications in economic complexity.
  • The novel MONEY index, incorporating multiple singular vectors from MC, offers a more nuanced measure of economic complexity.

Conclusions:

  • Matrix Completion provides a powerful framework for analyzing economic complexity and reconstructing RCA data.
  • The MONEY index represents a significant advancement in quantifying economic complexity, offering a more comprehensive approach than existing methods.
  • MC-based classifiers show improved predictive accuracy for economic advantage discrimination.