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

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.

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