This paper discusses that Special Drawing Rights (SDRs) have been allocated by the IMF to members that are participants in the SDR Department (at the time of allocation) in proportion to their quotas in the IMF. Six allocations, totaling 21.4 billion SDR, were made by the IMF in 1970, 1971, 1972, 1979, 1980, and 1981. In addition, a general allocation of 161.2 billion SDR was made on August 28, 2009, and a special allocation of 21.5 billion SDR was made on September 9, 2009. The IMF cannot allocate SDRs to itself, but can receive them from members through various financial transactions and operations. Entities authorized to conduct transactions in SDRs are the IMF itself, participants in the SDR Department, and other prescribed holders. The SDR can be used for a wide range of transactions and operations, including for acquiring other members’ currencies, settling financial obligations, making donations, and extending loans.
This November 2018 issue of International Financial Statistics (IFS) is a standard source of statistics on all aspects of international and domestic finance. IFS, Balance of Payments Statistics, Direction of Trade Statistics, and Government Finance Statistics are available on CD-ROM by annual subscription. The CD-ROMs incorporate a Windows-based browser facility, as well as a flat file of the database in scientific notation. The country tables normally include data on a country’s exchange rates, IMF position, international liquidity, monetary statistics, interest rates, prices, production, labor, international transactions, government accounts, national accounts, and population. Selected series, including data on IMF accounts, international reserves, and international trade, are drawn from the country tables and published in world tables as well. The monthly printed issue of IFS reports current monthly, quarterly, and annual data, while the yearbook reports 12 observations of annual data. In IFS, exchange rates are expressed in time series of national currency units per SDR and national currency units per US dollar, or vice versa.
The Annual Report on Exchange Arrangements and Exchange Restrictions has been published by the IMF since 1950. It draws on information available to the IMF from a number of sources, including that provided in the course of official staff visits to member countries, and has been prepared in close consultation with national authorities.
In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals. The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex.
Financial Soundness Indicators (FSIs) are measures that indicate the current financial health and soundness of a country's financial institutions, and their corporate and household counterparts. FSIs include both aggregated individual institution data and indicators that are representative of the markets in which the financial institutions operate. FSIs are calculated and disseminated for the purpose of supporting macroprudential analysis--the assessment and surveillance of the strengths and vulnerabilities of financial systems--with a view to strengthening financial stability and limiting the likelihood of financial crises. Financial Soundness Indicators: Compilation Guide is intended to give guidance on the concepts, sources, and compilation and dissemination techniques underlying FSIs; to encourage the use and cross-country comparison of these data; and, thereby, to support national and international surveillance of financial systems.
The world economy is experiencing a very strong but uneven recovery, with many emerging market and developing economies facing obstacles to vaccination. The global outlook remains uncertain, with major risks around the path of the pandemic and the possibility of financial stress amid large debt loads. Policy makers face a difficult balancing act as they seek to nurture the recovery while safeguarding price stability and fiscal sustainability. A comprehensive set of policies will be required to promote a strong recovery that mitigates inequality and enhances environmental sustainability, ultimately putting economies on a path of green, resilient, and inclusive development. Prominent among the necessary policies are efforts to lower trade costs so that trade can once again become a robust engine of growth. This year marks the 30th anniversary of the Global Economic Prospects. The Global Economic Prospects is a World Bank Group Flagship Report that examines global economic developments and prospects, with a special focus on emerging market and developing economies, on a semiannual basis (in January and June). Each edition includes analytical pieces on topical policy challenges faced by these economies.
IMF Financial Operations 2018 provides a broad introduction to how the IMF fulfills its mission through its financial activities. It covers the financial structure and operations of the IMF and provides background detail on the financial statements. It reviews the IMF's three main activities: lending, surveillance, and technical assistance.
Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.
