Using Machine Learning to Capture Heterogeneity in Trade Agreements

47 Pages Posted: 26 Mar 2021 Last revised: 6 Apr 2021

See all articles by Scott L. Baier

Scott L. Baier

Clemson University - John E. Walker Department of Economics

Narendra Regmi

University of Wisconsin-Whitewater

Date Written: March 25, 2021

Abstract

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.

Keywords: free trade agreements, machine learning, gravity model

JEL Classification: F10, F13

Suggested Citation

Baier, Scott Leonard and Regmi, Narendra, Using Machine Learning to Capture Heterogeneity in Trade Agreements (March 25, 2021). Mercatus Working Paper Series, Available at SSRN: https://ssrn.com/abstract=3812599 or http://dx.doi.org/10.2139/ssrn.3812599

Scott Leonard Baier (Contact Author)

Clemson University - John E. Walker Department of Economics ( email )

Clemson, SC 29634
United States
864-656-4534 (Phone)

Narendra Regmi

University of Wisconsin-Whitewater ( email )

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