Economic research and trade policy analysis

As digital trade becomes an increasingly vital component of the global economy, identifying the impact of wider international cooperation on digital trade issues becomes a growing policy imperative. This paper seeks to provide a quantitative analysis of the potential economic impacts of concluding the WTO Agreement on E-commerce (AoE).

This paper introduces the Trade Policy Activity (TPA) Index, a novel indicator measuring evolving global trade policy dynamics since the Global Financial Crisis. Using a Dynamic Factor Model on comprehensive trade policy data covering 197 countries and territories, we document a structural shift around 2019 with a substantial expansion in the use of trade policies.

On average, wages of female workers are lower than wages of male workers. In this paper, we explore to what extent a gender bias in trade costs explains this gender wage gap and how different policy reforms could lower it.

We employ the WTO Global Trade Model (GTM) to generate quantitative projections on the expected long-run impact of digitalization on global trade patterns. Five trends are modelled: (i) adoption of artificial intelligence which raises productivity growth;(ii) digitalization reducing trade costs, (iii) the shift to online sales (e-commerce), (iv) the reduced need for physical face-to-face interaction leading to lower trade costs; and (v) changes in data policies in response to these technologies which also impacts trade costs. We distinguish between a convergence scenario with larger productivity growth and trade cost reductions for low-income economies and a core scenario with uniform changes. The simulations generate three main sets of findings.

This paper explores the application of Natural Language Processing (NLP) techniques to automate Harmonized System (HS) tariff line transposition, employing a three-stage process: unique 1:1 tariff code matching (Round 1), exact description matching (Round 2), and “smart” description matching (Round 3) using Artificial Intelligence (AI) and lexical similarity methods paired with harmonized 6-digit concordance and cosine similarity. Similarity is calculated using either Term Frequency Inverse Document Frequency (TF-IDF) vectors or Sentence-BERT (SBERT) embeddings, comparing two scenarios: a straightforward case (Economy A) with standardized descriptions, and a complex case (Economy B), with more detailed technical descriptions.