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Using Machine Learning to Capture Heterogeneity in Trade Agreements

Author

Listed:
  • Scott L. Baier

    (Clemson University)

  • Narendra R. Regmi

    (University of WI–Whitewater)

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.

Suggested Citation

  • Scott L. Baier & Narendra R. Regmi, 2023. "Using Machine Learning to Capture Heterogeneity in Trade Agreements," Open Economies Review, Springer, vol. 34(4), pages 863-894, September.
    • Handle: RePEc:kap:openec:v:34:y:2023:i:4:d:10.1007_s11079-022-09685-3
    DOI: 10.1007/s11079-022-09685-3
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    More about this item

    Keywords

    Free trade agreements; Machine learning; Gravity model; Deep integration;
    All these keywords.

    JEL classification:

    • F10 - International Economics - - Trade - - - General
    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations

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