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

2024-07-10

AI Summary

This paper examines the heterogeneous effects of the Comprehensive Economic and Trade Agreement (CETA) on French firms’ exports, focusing on product-level intensity effects, product turnover dynamics, comparative advantage responses, multi-product portfolio reallocation, and trade diversion. Leveraging French customs microdata, we construct a firm–product–destination three-dimensional counterfactual matrix and propose a novel multidimensional counterfactual inference framework integrating causal machine learning with low-rank matrix completion. We find that CETA significantly increases export intensity; each newly exported product substitutes, on average, 0.96 existing products—indicating high product churn; products aligned with firms’ comparative advantage experience stronger positive effects; multi-product firms rebalance portfolios toward their top-exported products; and substantial general-equilibrium trade diversion toward Canada emerges. Our methodology advances the evaluation of deep trade agreements by delivering both a scalable counterfactual estimation framework and granular microeconomic evidence on firm- and product-level adjustment mechanisms.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Assessing CETA's impact using causal machine learning
• Analyzing firm-product-destination trade dynamics post-CETA
• Measuring trade diversion and product churning effects

Innovation

• Causal machine learning for trade impact analysis
• Matrix completion algorithm on customs data
• Multidimensional counterfactuals at firm level