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.
The paper quantifies these insights to characterize a country’s latent comparative advantages and produce export diversification recommendations, using machine learning algorithms that implement collaborativefiltering , an approach used widely by online commercial applications for their recommender systems. A recommender system based on collaborativefiltering uses the revealed preferences of a group of users to make predictions about the preferences of a user similar to the group. There are numerous applications of this approach in the e-commerce space
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.
-specific diversification strategy, or practical insights in guiding the structural change in exports.
In a recent study, Che (2020) proposes a novel method to operationalize the concept of comparative advantage and its evolution. It uses collaborativefiltering algorithms in machine learning most commonly applied to product recommendations in e-commerce, to produce export diversification recommendations that reflect a country’s latent comparative advantages and future potentials in export structure. Section 3 will go over the details of the methodology. But the basic intuition
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 collaborativefiltering 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
International Monetary Fund. Western Hemisphere Dept.
-targeted industrial policies can be beneficial to growth. Designing growth-friendly industrial policies, however, requires identifying areas of comparative advantage where there is untapped potential for diversification. This paper uses machine learning algorithms for collaborativefiltering to explore potential areas of export diversification for Uruguay.
B. Measuring Export Diversification
5. Throughout this paper, export diversification is measured by the number of export products a country has with high “revealed comparative advantage” (RCA) . The RCA score, first
International Monetary Fund. Asia and Pacific Dept
collaborativefiltering, identifies chemical products, machineries and food as promising sectors with diversification potential for Brunei (see Annex IV for details). Identifying policies that could facilitate the development of these and other export sectors could be a useful step towards more diversification, and thereby foster growth and macroeconomic stability. Staff estimates suggest that a significant diversification push could increase Brunei medium-term growth by over 1.5 percentage points, while reducing growth volatility by 0.5 percentage point (see Annex IV
International Monetary Fund. Asia and Pacific Dept
After successfully weathering the pandemic in 2020, Brunei was hit by new waves of COVID-19, with case numbers going up significantly and new lockdown measures imposed in H2 2021. Reduced activities in mining and LNG manufacturing, combined with the negative impact of new pandemic variants on domestic services, led to a slowdown in the economy. Real GDP contracted by 1.6 percent in 2021. For 2022, growth is projected to rebound to 1.2 percent, on the back of easing of mobility constraints and a positive terms of trade shock due to surges in O&G prices. Inflation, while remaining relatively low at 2.2 percent at end 2021, has increased in 2022 and pressures are expected to remain elevated in the short term, owing to supply disruptions and higher food and fuel prices. The economy continues to diversify, with double-digit growth of the food/agriculture sector and a new fertilizer sector commencing production. The risks to the outlook are tilted to the downside, due to potential new COVID-19 variants, increased global uncertainty associated with an escalation of the war in Ukraine, monetary tightening from the US and a larger-than-expected growth slowdown in China. On the upside, higher energy prices would further improve the terms of trade and restore fiscal positions in the short term, while partially contributing to build the buffers needed to ensure stronger intergenerational equity. Strong policy actions are needed to boost medium-term growth and foster resilience.