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Mr. Serkan Arslanalp, Mr. Marco Marini, and Ms. Patrizia Tumbarello

I. Motivation II. Automatic Identification System (AIS) Data: An Innovative Source for Tracking Vessel Traffic in Real Time III. Our Proposed Approach A. Filtering Port Calls Generating Trade Activity B. Deriving High-Frequency Indicators of Vessel Traffic IV. Quality Assurance of AIS-Based Indicators A. Maritime Statistics B. Trade Statistics V. Conclusions and Policy Implications References Box 1. Tracking the Arrival of a Liquefied Natural Gas Tanker in Malta Figures 1. A Snapshot of Global Vessel Traffic Based AIS Data 2. Defining

Mr. Serkan Arslanalp, Mr. Robin Koepke, and Jasper Verschuur

methodology for overcoming challenges with vessels’ Automatic Identification System (AIS) data to estimate trade. It includes the use of (i) appropriate filtering, backpropagating, historical averaging techniques to overcome possible measurement errors with AIS data; (ii) vessel-specific information to avoid estimating trade volumes linearly with draft changes; and (iii) use of domain expertise, including country/port specific information and liner shipping schedules, to improve the accuracy of estimates. Second, the approach outlined in this paper does not require access

Mr. Serkan Arslanalp, Mr. Marco Marini, and Ms. Patrizia Tumbarello
Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by comparing them with official statistics on trade and maritime statistics. If the challenges associated with port call data are overcome through appropriate filtering techniques, we show that these emerging “big data” on vessel traffic could allow statistical agencies to complement existing data sources on trade and introduce new statistics that are more timely (real time), offering an innovative way to measure trade activity. That, in turn, could facilitate faster detection of turning points in economic activity. The approach could be extended to create a real-time worldwide indicator of global trade activity.
Mr. Serkan Arslanalp, Mr. Marco Marini, and Ms. Patrizia Tumbarello

on the filtered ships: a “cargo number” indicator that counts the number of ships visiting ports, and a “cargo load” indicator that combines information in the AIS data about the size of the vessel (i.e., deadweight tonnage) and changes in its cargo load (i.e., draught) to derive a trade volume index. 2 Malta is an excellent benchmark for several reasons. First, it is a small and open economy and is highly dependent on external trade. Second, it depends heavily on imports for its industries and consumers and about two-thirds of its imports are carried by ship

International Monetary Fund. Research Dept.

the movement of goods across the globe? What are the benefits and challenges of using big data to produce real-time information about trade? In what ways can countries use this source to complement traditional sources of trade statistics? To answer these questions, a team of IMF economists employed a more structured version of the AIS data, containing “port-calls data.” Port-calls data combine ship positions and port boundaries to track the arrival and departure of ships in a port. The economists used Malta-an island state in the European Union, with a population

Andras Komaromi

Front Matter Page Asia and Pacific Department and Innovation Lab Unit Contents I. Introduction II. A Shift-Share Design on High-Frequency Trade Data III. High-Frequency Data and Variable Construction IV. Results V. Indirect Supply-Chain Effects Figures 1. Lockdown Exposure and Import Growth: The cases of Korea and the U.S. 2. Construction of Port-to-Port Trade Volumes from AIS data 3. The Distribution of Travel Times in Shipping 4. Lockdown Spillovers: Strong but Short-Lived 5. Ratio of Actual v. Counterfactual World Trade

Mr. Serkan Arslanalp, Mr. Robin Koepke, and Jasper Verschuur
This paper proposes an easy-to-follow approach to track merchandise trade using vessel data and applies it to Pacific island countries. Pacific islands rely heavily on imports and maritime transport for trade. They are also highly vulnerable to climate change and natural disasters that pose risks to ports and supply chains. Using satellite-based vessel tracking data from the UN Global Platform, we construct daily indicators of port and trade activity for Pacific island countries. The algorithm significantly advances estimation techniques of previous studies, particularly by employing ways to overcome challenges with the estimation of cargo payloads, using detailed information on shipping liner schedules to validate port calls, and applying country-specific information to define port boundaries. The approach can complement and help fill gaps in official data, provide early warning signs of turning points in economic activity, and assist policymakers and international organizations to monitor and provide timely responses to shocks (e.g., COVID-19).
Andras Komaromi, Mr. Diego A. Cerdeiro, and Yang Liu

these devices – which include information about ship type, position, speed, draught, etc. – are visible to nearby ships so as to avoid collisions, and are also collected by terrestrial receivers (if the ship is near a shore) and commercial satellites (if the ship is in the deep oceans). CKLS show how different machine-learning techniques can be used to construct port-to-port voyages and estimates of trade volumes based on AIS data. We use their estimates that build on over two billion AIS messages collected between January 1 st 2015 and December 31 st 2021. To

International Monetary Fund. Research Dept.
It has been two years since the trade tensions erupted and not only captured policymakers’ but also the research community’s attention. Research has quickly zoomed in on understanding trade war rhetoric, tariff implementation, and economic impacts. The first article in the December 2019 issue sheds light on the consequences of the recent trade barriers.
Andras Komaromi

commercial satellites (if the ship is in the deep oceans). Cerdeiro, Komaromi, Liu and Saeed (2020 ; CKLS henceforth) show how different machine-learning techniques can be used to construct port-to-port voyages and estimates of trade volumes based on AIS data. We use their estimates that build on over one billion AIS messages collected between January 1 st 2015 and June 30 th 2020. To make this paper self-contained, we briefly illustrate here the process of going from the raw AIS messages to port-to-port volume estimates. The reader is referred to CKLS for further