Beneath the Surface : Deep Trade Agreements and the Potential of a Deeper AfCFTA

This paper examines what makes some trade agreements more effective at promoting trade than others and applies this question to the case of the African continent. The analysis uses a clustering algorithm to classify worldwide trade agreements into three categories and estimates their trade effects within a structural gravity framework. This allows ranking clusters according to their estimated trade effects as shallow, medium, and deep. The analysis then uses feature-attribution methods and counterfactual policy combinations to identify the provisions and policy configurations most strongly associated with deep classification and applies this approach to the African Continental Free Trade Area. The ambition of this agreement is to harmonize existing regional agreements into a single continent-wide market: it is currently classified as medium in this paper's baseline. The general-equilibrium counterfactual analysis shows that, if implemented as a deep agreement, the African Continental Free Trade Area would generate substantial additional trade gains, along with an average gross domestic product gain of 0.7 percent for the African economies in the sample. The classification analysis further indicates that tighter disciplines on trade-defense instruments, stronger legal enforceability, and visa-related provisions are among the policies most closely associated with deep agreements.