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

. Originally, AIS was designed to monitor vessel location and movement primarily for traffic management, collision avoidance, and other safety applications for port authorities and harbor masters. However, over time, there have been many other uses. UNCTAD, for example, already provides statistics on port calls building on AIS data provided by MarineTraffic. In addition, research on the use of AIS for official statistics is part of the European statistical system’s ESSnet Big Data project—in fact, Denmark and Norway already publish daily number of vessels visiting Danish and

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).
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

(i.e., mainly export or import). Finally, we highlight that vessel traffic data should be used with caution when the coverage of AIS-receiving stations (terrestrial or satellite) is poor near the country’s ports or in the case of trade sanctions that may cause vessels to switch off their AIS transponders during trade activity with sanctioned countries. In doing so, we propose a two-step approach to nowcast trade flows in real time: developing a filter to identify cargo ships involved in trade activity in port calls data, and; producing two indicators based

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

IMF management. Abstract 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, the emerging big data on vessel traffic could enable statistical

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

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

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

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

introduces autocorrelation in our error term – e.g. any ship arriving unexpectedly at time t will reverberate in our transformed data for six additional days. Econometrically, we address the resulting inference problem by clustering standard errors at the country-level which are robust to autocorrelation. 8 While the raw-AIS data sample starts on January 1 st , 2015, the classification of a port call as imports requires knowing that the previous port is in fact located in a different country. To avoid start-point estimation problems, we censor our estimates before

Andras Komaromi
World trade contracted dramatically during the global economic crisis induced by the COVID-19 pandemic. Disruptions in international supply chains were widely reported as governments imposed containment measures (lockdowns) to halt the spread of the disease. At the same time, demand declined as households and firms scaled back spending. This paper attempts to disentangle the supply and demand channels in trade by quantifying the causal effect of supply spillovers from lockdowns. We utilize a novel dataset of daily bilateral seaborne trade, and design a shift-share identification strategy that leverages geography-induced cargo delivery lags to track the transmission of supply disruptions across space. We find strong but short-lived supply spillovers of lockdowns through international trade. Moreover, the evidence is suggestive of the downstream propagation of countries’ lockdowns through global supply chains.