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

Natasha Che and Xuege Zhang

No 2022/075, IMF Working Papers from International Monetary Fund

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.

Keywords: export diversification; comparative advantage; machine learning; collaborative filtering; economic growth; international trade; export structure; export portfolio recommendation; export recommendation system; performance export portfolio; export potential; Exports; Comparative advantage; Export diversification; Human capital; Total factor productivity; Global; East Asia (search for similar items in EconPapers)
Pages: 52
Date: 2022-04-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-int and nep-sea
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