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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.
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.

Tuvalu

International Monetary Fund. Asia and Pacific Dept

Published: 2023-07-21

Total Pages: 81


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The 2023 Article IV Consultation discusses that a successful vaccination strategy allowed Tuvalu to lift coronavirus disease (COVID) containment measures at the end of 2022, but the economic cost of the pandemic has been significant. Real gross domestic product growth was -4.3 percent in 2020, with at-the-border containment measures leading to delays in much-needed infrastructure projects. Growth is expected to accelerate as the lifting of COVID restrictions leads to the resumption of construction activity, shipping bottlenecks ease and the trade and hospitality sectors recover. Tuvalu is among the countries most vulnerable to the effects of climate change; its remote economy is dominated by the public sector; and its revenue base is narrow, with reliance on donor commitments further complicating fiscal planning. The economic setback due to the pandemic makes addressing these significant structural challenges more difficult. The report recommends promoting fiscal sustainability and building buffers by mobilizing revenues and rationalizing current expenditures.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
The latest World Economic Outlook reports signs that policy tightening is starting to cool activity despite core inflation proving persistent. Risks are more balanced as banking sector stress has receded, but they remain tilted to the downside. Monetary policy should stay the course to bring inflation to target, while fiscal consolidation is needed to tackle soaring debts. Structural reforms are crucial to revive medium-term growth prospects amid constrained policy space.
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This series contains the decisions of the Court in both the English and French texts.
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As the nation's economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices. Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation's S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.