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
Additional contact information
Valentina Corradi: University of Surrey
Nadia Rocha: World Bank

No 521, School of Economics Discussion Papers from School of Economics, University of Surrey

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 develop a new method to estimate the impact of individual provisions on trade flows that does not require ad hoc assumptions on how to aggregate individual provisions. Building on recent developments in the machine learning and variable selection literature, we propose data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. We find that provisions related to antidumping, competition policy, technical barriers to trade, and trade facilitation are associated with enhancing the trade-increasing effect of trade agreements.

JEL-codes: F14 F15 F17 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2021-03
New Economics Papers: this item is included in nep-big and nep-int
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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https://repec.som.surrey.ac.uk/2021/DP05-21.pdf (application/pdf)

Related works:
Working Paper: Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements (2022)
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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Persistent link: https://EconPapers.repec.org/RePEc:sur:surrec:0521

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