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Machine learning in international trade research - evaluating the impact of trade agreements

Author

Listed:
  • Holger Breinlich
  • Valentina Corradi
  • Nadia Rocha
  • Michele Ruta
  • J.M.C. Santos Silva
  • Tom Zylkin

Abstract

Modern trade agreements contain a large number of provisions in addition to tariff reductions, in areas as diverse as services trade, competition policy, trade-related investment measures, or public procurement. Existing research has struggled with overfitting and severe multicollinearity problems when trying to estimate the effects of these provisions on trade flows. Building on recent developments in the machine learning and variable selection literature, this paper proposes data-driven methods for selecting the most important provisions and quantifying their impact on trade flows, without the need of making ad hoc assumptions on how to aggregate individual provisions. The analysis finds that provisions related to antidumping, competition policy, technical barriers to trade, and trade facilitation are associated with enhancing the trade-increasing effect of trade agreements.

Suggested Citation

  • Holger Breinlich & Valentina Corradi & Nadia Rocha & Michele Ruta & J.M.C. Santos Silva & Tom Zylkin, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," CEP Discussion Papers dp1776, Centre for Economic Performance, LSE.
    • Handle: RePEc:cep:cepdps:dp1776
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    References listed on IDEAS

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    Cited by:

    1. Gordeev, Stepan & Steinbach, Sandro, 2024. "Determinants of PTA design: Insights from machine learning," International Economics, Elsevier, vol. 178(C).
    2. Sharadendu Sharma & Yadnesh P. Mundhada & Rahul Arora, 2023. "Which Combination of Trade Provisions Promotes Trade in Value‐Added? An Application of Machine Learning to Cross‐Country Data," Economic Papers, The Economic Society of Australia, vol. 42(4), pages 332-346, December.
    3. Bergstrand, Jeffrey H. & Clance, Matthew W. & Santos Silva, J.M.C., 2025. "The tails of gravity: Using expectiles to quantify the trade-margins effects of economic integration agreements," Journal of International Economics, Elsevier, vol. 157(C).
    4. Kim, Dongin & Steinbach, Sandro, 2022. "Preferential Trading in Agricultural and Food Products: New Insights from a Structural Gravity Analysis and Machine Learning," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322200, Agricultural and Applied Economics Association.
    5. Jeffrey H. Bergstrand & Stephen R. Cray & Antoine Gervais, 2023. "Increasing Marginal Costs, Firm Heterogeneity,and the Gains from "Deep" International Trade Agreements," Cahiers de recherche 23-01, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    6. Bergstrand, Jeffrey H. & Cray, Stephen R. & Gervais, Antoine, 2023. "Increasing marginal costs, firm heterogeneity, and the gains from “deep” international trade agreements," Journal of International Economics, Elsevier, vol. 144(C).
    7. Nathapornpan Piyaareekul Uttama, 2023. "Revisiting the Impacts of COVID-19 Government Policies and Trade Measures on Trade Flows: A Focus on RCEP Nations," Working Papers DP-2023-17, Economic Research Institute for ASEAN and East Asia (ERIA).
    8. Kaleb Abreha & Raymond Robertson, 2023. "Heterogeneous trade agreements and adverse implications of restrictive rules of origin: Evidence from apparel trade," The World Economy, Wiley Blackwell, vol. 46(12), pages 3482-3510, December.

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    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F15 - International Economics - - Trade - - - Economic Integration
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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