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The heterogeneous impact of the EU-Canada agreement with causal machine learning

Author

Listed:
  • Lionel Fontagné

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Francesca Micocci

    (IMT - School for Advanced Studies Lucca)

  • Armando Rungi

    (IMT - School for Advanced Studies Lucca)

Abstract

This paper introduces a causal machine learning approach to investigate the impact of the EU-Canada Comprehensive Economic Trade Agreement (CETA). We propose a matrix completion algorithm on French customs data to obtain multidimensional counterfactuals at the firm, product and destination levels. We find a small but significant positive impact on average at the product-level intensive margin. On the other hand, the extensive margin shows product churning due to the treaty beyond regular entry-exit dynamics: one product in eight that was not previously exported substitutes almost as many that are no longer exported. When we delve into the heterogeneity, we find that the effects of the treaty are higher for products at a comparative advantage. Focusing on multiproduct firms, we find that they adjust their portfolio in Canada by reallocating towards their first and most exported product due to increasing local market competition after trade liberalization. Finally, multidimensional counterfactuals allow us to evaluate the general equilibrium effect of the CETA. Specifically, we observe trade diversion, as exports to other destinations are re-directed to Canada.

Suggested Citation

  • Lionel Fontagné & Francesca Micocci & Armando Rungi, 2025. "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Working Papers halshs-04913313, HAL.
  • Handle: RePEc:hal:wpaper:halshs-04913313
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04913313v1
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