Servicification of <scp>GVCs</scp> through deep service provisions: Uncovering new insights from structural gravity and machine learning

Abstract In recent decades, there has been a notable increase in linkages of services in decoupling global value chains (GVCs) and a surge in regulatory mechanisms embedded in service provisions in trade agreements. Existing literature tried to empirically link the impact of such service provisions on GVC‐related services, but none focused on identifying relevant service provisions. This study is a novel attempt in this direction using a machine learning algorithm augmented in gravity modelling. Building on the identified service provisions, the study quantifies their impact on GVC‐related services conditioned on the countries' income levels. The study also conducts the general equilibrium analysis by simulating a scenario incorporating identified service provisions in the India‐ASEAN trade agreement. The analysis finds that few service provisions exist that enhance the share of foreign service inputs in manufacturing exports of the countries involved in GVC‐related service participation. Moreover, the impact is heterogeneous regarding benefits to the developing countries as a destination of service‐value added. Finally, the study shows that introducing selected service provisions in existing trade agreements can potentially increase welfare and service trade.