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Ms. Natasha X Che and Xuege Zhang

do not take a stand ex-ante regarding whether a specific country should consider a structural change or a diversification. The machine-learning-based export recommendations can provide useful guidance on both. In this paper, our primary goal is to test the hypothesis that export product recommendations based on a collaborative filtering algorithm indeed reflect what a given country’s export structure could look like to help it grow better at any given time. To do this, we first use a product-based k-nearest neighbors algorithm similar to Che (2020) to make

Ms. Natasha X Che and Xuege Zhang
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.