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- Title
Using Machine Learning to Capture Heterogeneity in Trade Agreements.
- Authors
Baier, Scott L.; Regmi, Narendra R.
- Abstract
This paper uses 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 on 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.
- Subjects
MACHINE learning; INTERNATIONAL arbitration; INTERNATIONAL trade; COMMERCIAL treaties; TRADE regulation; IMPORT quotas; PHYTOSANITATION
- Publication
Open Economies Review, 2023, Vol 34, Issue 4, p863
- ISSN
0923-7992
- Publication type
Article
- DOI
10.1007/s11079-022-09685-3