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High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning

Author

Listed:
  • Ms. Natasha X Che
  • Xuege Zhang

Abstract

This paper studies the relationship between export structure and growth performance. We design an export recommendation system using a collaborative filtering algorithm based on countries' revealed comparative advantages. The system is used to produce export portfolio recommendations covering over 190 economies and over 30 years. We find that economies with their export structure more aligned with the recommended export structure achieve better growth performance, in terms of both higher GDP growth rate and lower growth volatility. These findings demonstrate that export structure matters for obtaining high and stable growth. Our recommendation system can serve as a practical tool for policymakers seeking actionable insights on their countries’ export potential and diversification strategies that may be complex and hard to quantify.

Suggested Citation

  • Ms. Natasha X Che & Xuege Zhang, 2022. "High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning," IMF Working Papers 2022/075, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2022/075
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