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

April 29, 2022

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Format: Chicago

Natasha X Che, and Xuege Zhang. High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning, (USA: International Monetary Fund, 2022) accessed November 13, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Comparative advantage, Export diversification, Exports, Human capital, International trade, Labor, Production, Total factor productivity

Keywords: Collaborative filtering, Comparative advantage, Comparative advantage, East Asia, Economic growth, Export diversification, Export diversification, Export portfolio recommendation, Export potential, Export recommendation system, Export structure, Exports, Global, Human capital, International trade, Machine learning, Performance export portfolio, Total factor productivity

Publication Details

Pages:

52
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/075
Stock No:

WPIEA2022075
ISBN:

9798400207013
ISSN:

1018-5941

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