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

By Diego A. Cerdeiro, Andras Komaromi, Yang Liu, Mamoon Saeed

May 14, 2020

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Diego A. Cerdeiro, Andras Komaromi, Yang Liu, and Mamoon Saeed. World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch, (USA: International Monetary Fund, 2020) accessed November 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Economic sectors, Financial crises

Publication Details

Pages:

44
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/057
Stock No:

WPIEA2020057
ISBN:

9781513544106
ISSN:

1018-5941

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