Using Machine Learning to Capture Heterogeneity in Trade Agreements

被引:6
|
作者
Baier, Scott L. [1 ]
Regmi, Narendra R. [2 ]
机构
[1] Clemson Univ, John E Walker Dept Econ, Clemson, SC 29634 USA
[2] Univ WI Whitewater, Dept Econ, Whitewater, WI USA
关键词
Free trade agreements; Machine learning; Gravity model; Deep integration; INTERNATIONAL-TRADE; GRAVITY; INTEGRATION;
D O I
10.1007/s11079-022-09685-3
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:863 / 894
页数:32
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