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World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch

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Listed:
  • Mr. Diego A. Cerdeiro
  • Andras Komaromi
  • Yang Liu
  • Mamoon Saeed

Abstract

Maritime data from the Automatic Identification System (AIS) have emerged as a potential source for real time information on trade activity. However, no globally applicable end-to-end solution has been published to transform raw AIS messages into economically meaningful, policy-relevant indicators of international trade. Our paper proposes and tests a set of algorithms to fill this gap. We build indicators of world seaborne trade using raw data from the radio signals that the global vessel fleet emits for navigational safety purposes. We leverage different machine-learning techniques to identify port boundaries, construct port-to-port voyages, and estimate trade volumes at the world, bilateral and within-country levels. Our methodology achieves a good fit with official trade statistics for many countries and for the world in aggregate. We also show the usefulness of our approach for sectoral analyses of crude oil trade, and for event studies such as Hurricane Maria and the effect of measures taken to contain the spread of the novel coronavirus. Going forward, ongoing refinements of our algorithms, additional data on vessel characteristics, and country-specific knowledge should help improve the performance of our general approach for several country cases.

Suggested Citation

  • Mr. Diego A. Cerdeiro & Andras Komaromi & Yang Liu & Mamoon Saeed, 2020. "World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch," IMF Working Papers 2020/057, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:2020/057
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    Cited by:

    1. Pol Antràs, 2020. "De-Globalisation? Global Value Chains in the Post-COVID-19 Age," NBER Working Papers 28115, National Bureau of Economic Research, Inc.
    2. Phurichai Rungcharoenkitkul, 2021. "Macroeconomic effects of COVID‐19: A mid‐term review," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 439-458, October.
    3. Saskia Meuchelböck & Vincent Stamer, 2021. "Hochfrequenzdaten aus der Schifffahrt als Indikator für den deutschen Außenhandel [Economic headlights: High-frequency data from shipping as an indicator of German foreign trade]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 101(5), pages 403-404, May.
    4. Aaron Flaaen & Flora Haberkorn & Logan Lewis & Anderson Monken & Justin Pierce & Rosemary Rhodes & Madeleine Yi, 2023. "Bill of lading data in international trade research with an application to the COVID‐19 pandemic," Review of International Economics, Wiley Blackwell, vol. 31(3), pages 1146-1172, August.
    5. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. Pragyan Deb & Davide Furceri & Jonathan D. Ostry & Nour Tawk, 2022. "The Economic Effects of COVID-19 Containment Measures," Open Economies Review, Springer, vol. 33(1), pages 1-32, February.
    7. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    8. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
    9. Inferrera, Sergio, 2021. "Globalisation in Europe: Consequences for the business environment and future patterns in light of Covid-19," IWH-CompNet Discussion Papers 2/2021, Halle Institute for Economic Research (IWH).
    10. Alexander Sandkamp & Vincent Stamer & Shuyao Yang, 2022. "Where has the rum gone? The impact of maritime piracy on trade and transport," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 751-778, August.
    11. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Jannsen, Nils & Kooths, Stefan & Meuchelböck, Saskia, 2021. "Deutsche Wirtschaft im Frühjahr 2021 - Erholung vor zweitem Anlauf [German Economy Spring 2021 - Recovery ready for second take off]," Kieler Konjunkturberichte 77, Kiel Institute for the World Economy (IfW Kiel).
    12. Hongyi Chen & Peter Tillmann, 2022. "Lockdown Spillovers," MAGKS Papers on Economics 202215, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    13. Richard A. Benton & J. Adam Cobb & Timothy Werner, 2022. "Firm partisan positioning, polarization, and risk communication: Examining voluntary disclosures on COVID‐19," Strategic Management Journal, Wiley Blackwell, vol. 43(4), pages 697-723, April.
    14. Antrà s, Pol, 2020. "De-Globalisation? Global Value Chains in the Post-COVID-19 Age," CEPR Discussion Papers 15462, C.E.P.R. Discussion Papers.
    15. Chen, Hongyi & Tillmann, Peter, 2023. "Lockdown spillovers," Journal of International Money and Finance, Elsevier, vol. 137(C).
    16. Fabian Stephany & Leonie Neuhäuser & Niklas Stoehr & Philipp Darius & Ole Teutloff & Fabian Braesemann, 2022. "The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    17. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.
    18. Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
    19. Kohei Matsumura & Yusuke Oh & Tomohiro Sugo & Koji Takahashi, "undated". "Nowcasting Economic Activity with Mobility Data," Bank of Japan Working Paper Series 21-E-2, Bank of Japan.

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