Measuring Global Trade Policy Activity

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. The TPA Index also identifies cyclical episodes of heightened activity and reveals interconnections between different types of measures. We are also able to identify systematic differences in trade policy deployment among groups of economies. Additionally, we employ MIDAS (Mixed Data Sampling) regressions with high-frequency data to develop nowcasting capabilities for trade policy activity, enabling real-time identification of potential policy shifts. These results contribute to the trade policy measurement literature and offer a tool for monitoring global trade policy developments in real time.

Authors

Publication Details

Journal
IMF Working Paper
Published
2025-10-01
DOI
https://doi.org/10.5089/9798229028356.001
Primary Topic
Global trade and economics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring Global Trade Policy Activity

Yuting Wei, Antonia Diakantoni, Monika Sztajerowska, Michèle Ruta et al.
IMF Working Paper
Global trade and economics
article

Measuring Global Trade Policy Activity

Yuting Wei, Antonia Diakantoni, Monika Sztajerowska, Michèle Ruta, Alexander Keck, Samuele Centorrino
article en

Abstract

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. The TPA Index also identifies cyclical episodes of heightened activity and reveals interconnections between different types of measures. We are also able to identify systematic differences in trade policy deployment among groups of economies. Additionally, we employ MIDAS (Mixed Data Sampling) regressions with high-frequency data to develop nowcasting capabilities for trade policy activity, enabling real-time identification of potential policy shifts. These results contribute to the trade policy measurement literature and offer a tool for monitoring global trade policy developments in real time.

IMF Working PaperVol. 2025(220)
Openalex Percentile: Top 51%
Global trade and economics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.