The number and complexity of trade agreements has boomed. In addition to traditional trade-related provisions, there is a proliferation of provisions addressing other issues, which do not necessarily directly regulate trade, yet have a significant impact on it.
In this context, ESCAP developed a web-based tool that allows users to efficiently map topics or provisions in texts of trade agreements. Currently, there are 463 trade agreements in the searchable database, including 237 involving at least one economy from the Asia-Pacific Region. The algorithm is based on detection of keyword combinations in the articles of trade agreements. It allows to accommodate for the variability of the natural language, as well as to map multiple categorized provisions or topics. As the final output, it generates datasets (spreadsheet format) that can be used in quantitative research or to create insightful visualizations.
The objective of this course is to teach how to use the online tool so that researchers can make the best use of what it has to offer by conducting automated text analysis and generating quantitative datasets. This course consists of three modules consisting of video lectures, quizzes, and two live sessions.
A basic understanding of the purpose and structure of trade agreements, topics of sustainable development; and reliable access to stable internet connection.
About the course conveyors
Maria Semenova is a consultant with Trade Policy and Facilitation Section (TIID) of the Trade, Investment and Innovation Division (TIID) of ESCAP. She developed the customizable algorithm for automated mapping of provisions in trade agreements used in generating the datasets that informed the discussion on regional trade agreements as a tool to promote climate-smart trade featured in Asia Pacific Trade and Investment Report (APTIR) 2021, fed into the construction of the Climate-smart Trade and Investment Index (SMARTII), and was used to generate a variety of datasets that fed into a few working papers and into this year’s APTIR 2023. Building on that algorithm, she developed this online tool which can help generate similar customizable datasets and which is the focus of this training.
Alexey Kravchenko is Economic Affairs Officer, TPFS, TIID, ESCAP. Dr. Kravchenko started teaching R for econometric analysis in 2009 at the University of Waikato, New Zealand, at graduate and undergraduate level. Since joining ESCAP, he’s delivered multiple R-based training, as well as authored and co-authored online courses and training material on R, including the code for The Gravity Model of International Trade: A User Guide (R version), ESCAP Online Training on Using R for Trade Analysis (https://r.tiid.org/), and most recently has been involved in the development of ESCAP’s Trade Intelligence and Negotiation Advisor (TINA, https://tina.trade).