March 25, 2021

Using Machine Learning to Capture Heterogeneity in Trade Agreements

Contact us
To speak with a scholar or learn more on this topic, visit our contact page.

In this paper, we employ machine learning techniques to capture heterogeneity in free trade agreements. The tools of machine learning allow us to quantify several features of trade agreements, including volume, comprehensiveness, and legal enforceability. Combining machine learning results with gravity analysis of trade, we find that more comprehensive agreements result in larger estimates of the impact of trade agreements. In addition, we identify the policy provisions that have the most substantial effect in creating trade flows. In particular, legally binding provisions on antidumping, capital mobility, competition, customs harmonization, dispute settlement mechanism, e-commerce, environment, export and import restrictions, freedom of transit, investment, investor-state dispute settlement, labor, public procurement, sanitary and phytosanitary measures, services, technical barriers to trade, telecommunications, and transparency tend to have the largest trade creation effects.

Read more

This paper is one of seven published as part of the Policy Analytics Symposium