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© 2022 International Monetary Fund

WP/22/75

IMF Working Paper

Asia & Pacific Department

High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning Prepared by Natasha Che, Xuege Zhang*

Authorized for distribution by Shanaka Jayanath Peiris

April 2022

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.

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.

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

Natasha Che, Xuege Zhang

Abstract

This paper studies the relationship between export structure and growth perfor- mance. 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

JEL Codes: F1, F4, O1, O4

*

The authors would like to thank Davide Furceri and participants in the APD and WHD departmental seminars for their helpful comments. The views are entirely our own.

*

International Monetary Fund. Email: nche@imf.org

Tepper School of Business, Carnegie Mellon University. Email: xuegez@andrew.cmu.edu

High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning
Author: Ms. Natasha X Che and Xuege Zhang