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
相关论文
共 50 条
  • [41] Numerical investigation of carbon dioxide capture using nanofluids via machine learning
    Feng, Li
    Zhu, Junren
    Jiang, Zhenzhen
    JOURNAL OF CLEANER PRODUCTION, 2024, 450
  • [42] Overlapped fingerprint image capture and separation using digital holography and machine learning
    Cho, Jaebum
    Lee, Byounghyo
    Yoo, Dongheon
    Lee, Byoungho
    TENTH INTERNATIONAL CONFERENCE ON INFORMATION OPTICS AND PHOTONICS, 2018, 10964
  • [43] Algorithmic assessment of shoulder function using smartphone video capture and machine learning
    Darevsky, David M.
    Hu, Daniel A.
    Gomez, Francisco A.
    Davies, Michael R.
    Liu, Xuhui
    Feeley, Brian T.
    SCIENTIFIC REPORTS, 2023, 13 (01):
  • [44] Predicting CO2 capture of ionic liquids using machine learning
    Venkatraman, Vishwesh
    Alsberg, Bjorn Kare
    JOURNAL OF CO2 UTILIZATION, 2017, 21 : 162 - 168
  • [45] Key Frames Detection in Motion Capture Recordings Using Machine Learning Approaches
    Hachaj, Tomasz
    IMAGE PROCESSING AND COMMUNICATIONS CHALLENGES 8, 2017, 525 : 79 - 86
  • [46] Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning
    Zhang, Xiaojian
    Zhou, Zhengze
    Xu, Yiming
    Zhao, Xilei
    JOURNAL OF TRANSPORT GEOGRAPHY, 2024, 114
  • [47] Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning
    Brand, Jennie E.
    Xu, Jiahui
    Koch, Bernard
    Geraldo, Pablo
    SOCIOLOGICAL METHODOLOGY, VOL 51, ISSUE 2, 2021, 51 (02): : 189 - 223
  • [48] Using machine learning to uncover heterogeneity of beta blocker response in heart failure
    Tison, Geoffrey H.
    CELL REPORTS MEDICINE, 2022, 3 (01)
  • [49] Mapping tissue heterogeneity in solid tumours using PET–MRI and machine learning
    Nature Biomedical Engineering, 2023, 7 : 969 - 970
  • [50] Toward smart carbon capture with machine learning
    Rahimi, Mohammad
    Moosavi, Seyed Mohamad
    Smit, Berend
    Hatton, T. Alan
    CELL REPORTS PHYSICAL SCIENCE, 2021, 2 (04):