Which Combination of Trade Provisions Promotes Trade in Value-Added? An Application of Machine Learning to Cross-Country Data

Abstract

Over time, the trade agreements are witnessing a substantial change in their provisions by encompassing provisions beyond their conventional trade domain, such as labour market regulations, environmental regulations and competition policies. Theoretically, studies argued the role of signing an agreement with deep provisions to promote trade in value-added, but empirical verification in favour of a few is rarely available. The present study attempts to identify this set of provisions included in deep trade agreements (DTAs) that positively impact the bilateral trade in value added. Using the traditional gravity model framework and its estimation through modern econometric and machine learning tools, the study shows that incorporating provisions relating to establishing and preserving economic rights in trade agreements promotes trade in value-added among member countries. Notably, the study found the combination of three main policy areas: technical barriers to trade, competition policy and labour market regulations. Both econometric and machine learning methods confirm the significant impact of these three provisions. Understanding the significance of specific provisions holds relevance in the current scenario where major trading economies are calibrating trade agreements. From the policy perspective, disentangling a set of provisions might be relevant for designing and negotiating trade agreements.