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Measuring Global Trade Policy Activity

Author

Listed:
  • Samuele Centorrino
  • Antonia Diakantoni
  • Alexander Keck
  • Michele Ruta
  • Monika Sztajerowska
  • Yuting Wei

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.

Suggested Citation

  • Samuele Centorrino & Antonia Diakantoni & Alexander Keck & Michele Ruta & Monika Sztajerowska & Yuting Wei, 2025. "Measuring Global Trade Policy Activity," IMF Working Papers 2025/220, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2025/220
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    References listed on IDEAS

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    1. Hiau LooiKee & Alessandro Nicita & Marcelo Olarreaga, 2009. "Estimating Trade Restrictiveness Indices," Economic Journal, Royal Economic Society, vol. 119(534), pages 172-199, January.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Alexander Jaax & Frédéric Gonzales & Annabelle Mourougane, 2021. "Nowcasting aggregate services trade," OECD Trade Policy Papers 253, OECD Publishing.
    4. Jésus Fernández-Villaverde & Tomohide Mineyama & Dongho Song & Jesús Fernández-Villaverde, 2024. "Are We Fragmented Yet? Measuring Geopolitical Fragmentation and Its Causal Effects," CESifo Working Paper Series 11192, CESifo.
    5. Pablo Fajgelbaum & Pinelopi Goldberg & Patrick Kennedy & Amit Khandelwal & Daria Taglioni, 2024. "The US-China Trade War and Global Reallocations," American Economic Review: Insights, American Economic Association, vol. 6(2), pages 295-312, June.
    6. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    7. Giordani, Paolo E. & Rocha, Nadia & Ruta, Michele, 2016. "Food prices and the multiplier effect of trade policy," Journal of International Economics, Elsevier, vol. 101(C), pages 102-122.
    8. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    9. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    10. Hites Ahir & Nicholas Bloom & Davide Furceri, 2022. "The world uncertainty index," CEP Discussion Papers dp1842, Centre for Economic Performance, LSE.
    11. Bogetoft Pedersen, Peter & Diakantoni, Antonia, 2020. "Lessons learned and challenges ahead for the WTO Trade Monitoring exercise," WTO Staff Working Papers ERSD-2020-03, World Trade Organization (WTO), Economic Research and Statistics Division.
    12. Julia Estefania-Flores & Davide Furceri & Mrs. Swarnali A Hannan & Mr. Jonathan David Ostry & Mr. Andrew K. Rose, 2022. "A Measurement of Aggregate Trade Restrictions and their Economic Effects," IMF Working Papers 2022/001, International Monetary Fund.
    13. Simon Evenett & Matteo Fiorini & Johannes Fritz & Bernard Hoekman & Piotr Lukaszuk & Nadia Rocha & Michele Ruta & Filippo Santi & Anirudh Shingal, 2022. "Trade policy responses to the COVID‐19 pandemic crisis: Evidence from a new data set," The World Economy, Wiley Blackwell, vol. 45(2), pages 342-364, February.
    14. Nuno Limão & Patricia Tovar, 2018. "Policy choice: Theory and evidence from commitment via international trade agreements," World Scientific Book Chapters, in: Policy Externalities and International Trade Agreements, chapter 6, pages 179-198, World Scientific Publishing Co. Pte. Ltd..
    15. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    16. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    17. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    18. Mr. Diego A. Cerdeiro & Rachel J. Nam, 2018. "A Multidimensional Approach to Trade Policy Indicators," IMF Working Papers 2018/032, International Monetary Fund.
    19. Bown, Chad P. & Crowley, Meredith A., 2013. "Import protection, business cycles, and exchange rates: Evidence from the Great Recession," Journal of International Economics, Elsevier, vol. 90(1), pages 50-64.
    20. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    21. James E. Anderson & J. Peter Neary, 2005. "Measuring the Restrictiveness of International Trade Policy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012200, December.
    22. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    JEL classification:

    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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