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

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  • Breinlich, Holger
  • Corradi, Valentina
  • Rocha, Nadia
  • Ruta, Michele
  • Zylkin, Thomas
  • Santos Silva, JMC

Abstract

Modern trade agreements contain a large number of provisions besides 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. In this paper, we build on recent developments in the machine learning and variable selection literature to propose novel data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. The proposed methods have the advantage of not requiring ad hoc assumptions on how to aggregate individual provisions and offer improved selection accuracy over the standard lasso. We find 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 & Zylkin, Thomas & Santos Silva, JMC, 2022. "Machine Learning in International Trade Research - Evaluating the Impact of Trade Agreements," CEPR Discussion Papers 17325, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17325
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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

    Lasso; Machine learning; Preferential trade agreements; Deep trade agreements;
    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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