Modern preferential trade agreements contain a host of provisions that go beyond tariff liberalisation. By adapting techniques from the machine learning literature, this column develops data-driven methods for selecting the most trade-increasing provisions and quantifying their impact on trade. The results suggest that provisions related to technical barriers to trade, anti-dumping, trade facilitation, subsidies, and competition policy are associated with enhancing the trade-increasing effect of preferential trade agreements. Based on these findings, the effects of individual agreements can be estimated.
Using Machine Learning to Assess the Impact of Deep Trade Agreements