Intelligent Export Diversification: An Export Recommendation System with Machine Learning

August 28, 2020

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 presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.

Subject: Comparative advantage, Export diversification, Exports, International trade, Machine learning, National accounts, Personal income, Technology

Keywords: Africa, Caribbean, collaborative filtering, comparative advantage, country-product space, diversification strategy, East Asia, economic growth, export basket, Export diversification, export diversification recommendation, export diversification strategy, export diversificiation, export product, export structure, Exports, Global, international trade, KNN implementation, KNN recommender, machine learning, Personal income, price boom, product name, product-space literature, RCA export, SITC product lists, WP

Pages:
46
Volume:
2020
DOI:
https://doi.org/10.5089/9781513555959.001
Issue:
175
Series:
Working Paper No. 2020/175
Stock No:
WPIEA2020175
ISBN:
9781513555959
ISSN:
1018-5941

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