Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements

Holger Breinlich, Valentina Corradi, Nadia Rocha, Michele Ruta, João Santos Silva and Thomas Zylkin

No 17325, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Modern trade agreements contain a large number of provisions besides tariff reductions, in areas as diverse as services trade, competition policy, trade-related investment measures, or public procurement. Existing research has struggled with overfitting and severe multicollinearity problems when trying to estimate the effects of these provisions on trade flows. In this paper, we build on recent developments in the machine learning and variable selection literature to propose novel data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. The proposed methods have the advantage of not requiring ad hoc assumptions on how to aggregate individual provisions and offer improved selection accuracy over the standard lasso. We find that provisions related to technical barriers to trade, antidumping, trade facilitation, subsidies, and competition policy are associated with enhancing the trade-increasing effect of trade agreements.

Keywords: Lasso; Machine learning; Preferential trade agreements; Deep trade agreements (search for similar items in EconPapers)
JEL-codes: F14 F15 F17 (search for similar items in EconPapers)
Date: 2022-05
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Related works:
Working Paper: Machine learning in international trade research - evaluating the impact of trade agreements (2021)
Working Paper: Machine learning in international trade research - evaluating the impact of trade agreements (2021)
Working Paper: Machine Learning in International Trade Research ?- Evaluating the Impact of Trade Agreements (2021)
Working Paper: Machine Learning in International Trade Research: Evaluating the Impact of Trade Agreements (2021)
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