In 2025 tariffs moved to centre-stage. But how is trade policy changing globally and how do these changes compare to the past? Several existing indicators capture important dimensions of the global trade environment, including trade policy uncertainty (Caldara et al. 2020), geopolitical risk (Caldara and Iacoviello 2022), and world uncertainty (Ahir et al. 2022). Yet trade policy today spans many instruments – tariffs, subsidies, and non-tariff measures – and changes frequently, posing a measurement challenge.

In our recent work (Centorrino et al. 2025), we address this gap by constructing the Trade Policy Activity (TPA) Index – a new monthly indicator of global trade policy dynamics covering 197 countries and territories since the Global Financial Crisis. Drawing on two complementary data sources – the WTO Trade Monitoring Database and the Global Trade Alert – we apply a dynamic factor model that treats records of a wide range of policy measures as imperfect signals of latent global trade policy activity and extracts their common dynamics. A trend-cycle decomposition distinguishes long-term shifts from cyclical fluctuations, while a block structure separates the global signal from category-specific variation, with weights determined endogenously from the data. 

The new indicator reveals dynamics in trade policy activity that were not captured by other measures and offers a more nuanced picture of how global trade policy has evolved. 

A rising tide of policy activity

Figure 1 presents the headline result. The TPA Index (solid line) shows a rising trend in global trade policy activity (dashed line) punctuated by distinct peaks. Several episodes stand out. The 2018–2019 spike captures the escalation of US–China bilateral tariffs. The COVID-19 pandemic triggered a broad-based surge in 2020 as countries simultaneously restricted exports of essential goods and facilitated imports. The 2022 peak reflects the trade policy response to the war in Ukraine. And in 2025, renewed trade tensions coincided with a sharp rise in the index.

Figure 1 Trade Policy Activity (TPA) Index

Note: The TPA Index (solid black line) captures the global factor from a block-structured dynamic factor model. The dashed line shows the estimated trend. Shaded areas represent 90% confidence intervals from bootstrap resampling. The index is normalized so that its average equals zero over January 2010–December 2011. Data through October 2025.

Underlying these fluctuations, the trend accelerates from 2020 onward, with the 90% confidence interval remaining above zero from 2022 onward. This shift coincides with a greater use of trade policy measures to achieve trade and non-trade objectives, rising trade tensions, and geoeconomic fragmentation (Aiyar et al. 2023, Goldberg and Reed 2023, Gopinath et al. 2025).

The gap between trade restrictive and trade facilitating measures is widening

A key question is how different categories of trade policy contribute to the global pattern. Figure 2 decomposes the index by distinguishing facilitating measures – actions that reduce restrictiveness or ease trade – from other measures, and further separating the latter into strictly restrictive measures following Deardorff (2014) (e.g., tariff increases, bans, quotas) and other measures that may distort trade, composed largely of subsidies. 

Figure 2 The role of different trade policy categories

A) Facilitating and other measures

B) Facilitating, restrictive, and the rest

Note: Panel A shows counterfactual indices for facilitating and other (non-facilitating) measures. Panel B further breaks down the other category into restrictive measures – such as tariff increases, bans, quotas, following Deardorff (2014) – and remaining other measures, such as subsidies. Each line reflects the global factor estimated using baseline loadings for one category only. The level and scale are normalized for comparability across the indices (mean equals 0 in January 2010–December 2011.)

What patterns emerge? 

First, facilitating and non-facilitating measures display strong co-movement at the global level. During crises like COVID-19 and the war in Ukraine, both categories surged together as countries bundled policies. This co-movement is consistent with several mechanisms identified in the trade literature: crisis-driven policy bundling (Giordani et al. 2016), strategic interactions across trading partners (Bagwell and Staiger 1999), and instrument substitution where liberalisation in one area accompanies restrictions in another (Beverelli et al. 2019).

Second, certain episodes are marked by divergence across measure categories. During the 2018–2019 and especially the 2025 trade tensions, restrictive measures accelerated sharply while facilitating measures decelerated relative to the common signal. A block structure accommodates such variation, helping better capture the overall level of trade policy activity.

Third, the 2025 spike is explained predominantly by a rise in measures that restrict trade, reflecting the increased use of tariffs and export controls. Meanwhile, subsidies and other potentially distortive measures display the most broad-based increase throughout the sample, consistent with the documented rise in industrial policy (Evenett et al. 2024, Juhász et al. 2025). 

Larger economies lead trade policy activity, but the trend is broad-based

Another question is whether the rising tide of policy activity reflects the actions of a few large economies only or is a broader phenomenon. Figure 3 answers this by estimating the global factor separately for G20 and non-G20 economies.

Figure 3 TPA Index across country groups: G20 versus non-G20

Note: The global factor is estimated separately for G20 economies (black line) and non-G20 economies (dark gray line). The baseline (light gray) is shown for reference. The level and scale are normalized for comparability across the indices (mean equals 0 in January 2010–December 2011.)

Both groups record positive trends of trade policy activity, but with telling differences. The G20 upward trend begins earlier and shows more pronounced peaks during the 2018–2019 tariff escalation and in 2025. Non-G20 economies, by contrast, recorded larger spikes during the COVID-19 pandemic, reflecting the widespread adoption of trade measures by smaller economies. 

When we zoom in on the different categories of trade policy within each group, we find that the G20 economies show the sharpest spikes in restrictive measures in 2018–2019 and 2025, and this pattern persists when the index is instead weighted by economic size. Importantly, excluding the US and China confirms those countries’ important contribution but also reveals a broad-based acceleration in restrictive measures since 2022, pointing to a rise in trade restrictions that extends beyond the two largest economies that dominate the headlines.

What the TPA Index adds to existing measures

As noted in the introduction, several indicators capture important dimensions of the global trade environment. Indices of trade policy uncertainty (Caldara et al. 2020), geopolitical risk (Caldara and Iacoviello 2022), and world uncertainty (Ahir et al. 2022) capture salient events through text analysis of media and analyst reports. The geopolitical fragmentation index by Fernández-Villaverde et al. (2024) combines indicators across trade, financial, mobility, and political dimensions to capture broader global tensions. Finally, welfare-based indices provide foundational theory-consistent measures of trade restrictiveness at the country level (Anderson and Neary 2005, Kee et al. 2009).

The TPA Index complements these measures by tracking a different dimension: the flow of actual policy changes at monthly frequency, across a comprehensive set of instruments, including facilitating and restrictive measures, at a global level. Figure 4 illustrates how the indices relate. The TPA broadly co-moves with existing measures while showing deviations during episodes specifically associated with trade policy changes, such as the WTO Trade Facilitation Agreement.

 Figure 4 TPA Index and other measures

Note: The figure compares the TPA with the trade fragmentation index from Fernández-Villaverde et al. (2024, FVMS), the geopolitical risk (GPR) index from Caldara and Iacoviello (2022, CI), the trade policy uncertainty (TPU) index from Caldara et al. (2020, CIMPR), and the world trade uncertainty index (WTUI) from Ahir et al. (2022, ABF), all normalized for comparability.

Cointegration tests confirm that the TPA shares a long-run relationship with each of these indices while capturing substantial additional variation. Even relative to the most closely related measure, Fernández-Villaverde et al. (2024), roughly half of the TPA’s variation is distinct. The share of independent variation is even larger relative to news-based indices. This additional variation underscores the complementarity between these measures: the TPA captures dynamics that are specific to trade policy and that overlap, but only partially, with the uncertainty, risk, and geopolitical shifts that existing indicators track.

The value of an index 

The TPA Index provides, to our knowledge, the first parsimonious monthly measure of actual trade policy changes at the global level, and it serves two distinct audiences. 

For researchers, it opens several empirical applications. As a comprehensive measure of global trade policy conditions, the index can serve as a control variable in studies of specific policy changes, helping address omitted variable bias from the aggregate policy environment. Its monthly frequency makes it suitable for event studies and local projections. It can also be used to study how the aggregate policy environment moderates the international transmission of shocks or to revisit classic questions on the determinants of trade policy, including its cyclicality.

For policymakers and trade observers, the index offers something hard to come by: a timely, systematic read on the evolution of global trade policy. Rather than relying on measures in a few selected economies or of a particular type, the TPA aggregates policy changes across instruments and economies, extracting common dynamics. Its design, and robustness to rolling-window estimation, supports periodic updating and nowcasting applications using high-frequency data, making it a practical tool for monitoring the trade policy landscape.

Authors’ note: The views expressed are those of the authors and do not necessarily represent the views of the institutions at which they work.

For WTO authors: This paper/publication has been prepared under our own personal responsibility. The opinions expressed in this paper/publication are ours only. They do not represent the positions or opinions of the WTO or its Members and are without prejudice to Members’ rights and obligations under the WTO. Any errors are attributable to us as the authors. 

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