Text-As-Data Analysis Of Preferential Trade Agreements Mapping The Pta Landscape


In the trade economy, textual similarity can complement existing depths to further assess the impact of EPAs on trade flows, as highlighted by the Trans-Pacific Partnership Agreement (TPP) after the withdrawal of the United States of America. We argue that textual similarities are particularly appropriate for understanding differences in contract design and we note that the concept of PTA comprises a number of very heterogeneous agreements, which systematically differ in scope, content and language. In addition, unlike bilateral investment agreements (ILOs), which largely follow the development of country-by-country-by-developed agreements, ATPs are grouped into regional or interregional groups of similarly worded agreements. With multilateral negotiations in the World Trade Organization (WTO) deadlocked, the settlement of international economic governance has shifted to preferential trade agreements (EPA). To facilitate the scientific study of the fast-growing world of PTAs, this article introduces a body of readable, structured, readable and structured complete texts of 448 WTO-signed trade agreements, which are stored in a Github repository – the text of the Corpus Trade Agreements (ToTA). [Read more] Trade agreements are more heterogeneous as a group than bilateral investment agreements A textual comparison of 450 preferential trade agreements through an interactive roadmap. Trade agreements converge in regional or interregional clusters of similarly worded agreements The “Texts of Trade Agreements” (ToTA) project makes scientists and policy makers public using the most advanced text-as-data techniques to analyze it. In trade policy, text engineers can help trace the spread of contracting, as we illustrate by following the spread of NAFTA across the Pacific. The number of trade agreements has increased significantly since the early 1990s. Trade agreements cover more and more subjects and an average text of the treaty is now about ten times longer than it was 25 years ago.

This makes it increasingly difficult to analyze the content of trade agreements and their impact on international trade and well-being. Big data and textual data methods can help researchers, policy makers and other stakeholders better manage the increasing complexity of trade agreements. Finally, the textual similarity of commercial law may shed new light on normative convergence processes. Investment chapters in EPZs, for example, are generally seen as a contribution to the convergence of trade and investment law, but, as our analysis shows, the two areas can also be fragmented. Preferred trade agreements (ATPs) form a complex network that connects countries around the world. In this article, we present a corpus of PTA text and research tools for its automated fine grain analysis. Recent advances in computing allow for effective and effective analysis of content by treating text as data.