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Machine Learning in International Trade Research : Evaluating the Impact of Trade Agreements

Author

Listed:
  • Breinlich,Holger
  • Corradi,Valentina
  • Rocha,Nadia
  • Ruta,Michele
  • Santos Silva,J.M.C.
  • Zylkin,Tom
Abstract
Modern trade agreements contain a large number of provisions besides tariff reductions, inareas 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 theeffects of these provisions on trade flows. This paper builds on recent developments in the machine learning andvariable selection literature to propose novel data-driven methods for selecting the most important provisions andquantifying their impact on trade flows. The proposed methods have the advantage of not requiring ad hocassumptions on how to aggregate individual provisions and offer improved selection accuracy over the standard lasso.The analysis finds that provisions related to technical barriers to trade, antidumping, trade facilitation,subsidies, and competition policy are associated with enhancing the trade-increasing effect of trade agreements.

Suggested Citation

  • Breinlich,Holger & Corradi,Valentina & Rocha,Nadia & Ruta,Michele & Santos Silva,J.M.C. & Zylkin,Tom, 2021. "Machine Learning in International Trade Research : Evaluating the Impact of Trade Agreements," Policy Research Working Paper Series 9629, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9629
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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. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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).

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    More about this item

    Keywords

    International Trade and Trade Rules; De Facto Governments; Economics and Finance of Public Institution Development; State Owned Enterprise Reform; Public Sector Administrative and Civil Service Reform; Public Sector Administrative & Civil Service Reform; Democratic Government; Competition Policy; Competitiveness and Competition Policy; Trade Facilitation; Health and Sanitation;
    All these keywords.

    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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