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This paper explores 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. The International Financial Statistics (IFS) contains country tables for most IMF members, as well as for Anguilla, Aruba, the Central African Economic and Monetary Community (CEMAC), Curaçao, the currency union of Curaçao and Sint Maarten, the Eastern Caribbean Currency Union (ECCU), the euro area, Montserrat, the former Netherlands Antilles, Sint Maarten, the West African Economic Monetary Union (WAEMU), West Bank and Gaza, and some non-sovereign territorial entities for which statistics are provided internationally on a separate basis. Also, selected series are drawn from the country tables and published in area and world tables. 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.
This paper explores 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. The International Financial Statistics (IFS) contains country tables for most IMF members, as well as for Anguilla, Aruba, the Central African Economic and Monetary Community (CEMAC), Curaçao, the currency union of Curaçao and Sint Maarten, the Eastern Caribbean Currency Union (ECCU), the euro area, Montserrat, the former Netherlands Antilles, Sint Maarten, the West African Economic Monetary Union (WAEMU), West Bank and Gaza, and some non-sovereign territorial entities for which statistics are provided internationally on a separate basis. Also, selected series are drawn from the country tables and published in area and world tables. 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.
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.
This paper provides notes to the country tables in the monthly issues provide information about exceptions in the choice of the consumer price index and the period average exchange rate index. For a relatively small number of countries, notes in the country tables in the monthly issues indicate where alternative price indices, such as the wholesale/producer price index or a weighted average of several price indices, are used; where data constraints have made it necessary to use weighting schemes based on aggregate bilateral non-oil trade data; and where trade in services (such as tourism) has been taken into account. When a country joins the IMF, it is assigned a quota that fits into the structure of existing quotas. Quotas are considered in the light of the member’s economic characteristics, and taking into account quotas of similar countries. Quotas are reviewed at intervals of not more than five years. The reviews take account of changes in the relative economic positions of members and the growth of the world economy.
This paper discusses about countries where multiple exchange rates are in effect, IMF staff estimates of weighted average exchange rates are used in many cases. A weighted average exchange rate is constructed as an average of the various exchange rates, with the weights reflecting the share of trade transacted at each rate. The notes to the country tables in the monthly issues provide information about exceptions in the choice of the consumer price index and the period average exchange rate index. For a relatively small number of countries, notes in the country tables in the monthly issues indicate where alternative price indices, such as the wholesale/producer price index or a weighted average of several price indices, are used; where data constraints have made it necessary to use weighting schemes based on aggregate bilateral non-oil trade data; and where trade in services (such as tourism) has been taken into account.
During the past financial year, the IMF’s 189 member countries faced a number of pressing challenges. IMF work on these challenges - slower trade, declining productivity, gender inequality, inclusive growth, and debt management - is a central focus of this 2017 Annual Report.
The 2007–09 international financial crisis underscored the importance of reliable and timely statistics on the general government and public sectors. Government finance statistics are a basis for fiscal analysis and they play a vital role in developing and monitoring sound fiscal programs and in conducting surveillance of economic policies. The Government Finance Statistics Manual 2014 represents a major step forward in clarifying the standards for compiling and presenting fiscal statistics and strengthens the worldwide effort to improve public sector reporting and transparency.
The audited consolidated financial statements of the International Monetary Fund as of April 30, 2019 and 2018
Calls for a more people-focused approach to statistics on economic performance, and concerns about inequality, environmental impacts, and effects of digitalization have put welfare at the top of the measurement agenda. This paper argues that economic welfare is a narrower concept than well-being. The new focus implies a need to prioritize filling data gaps involving the economic welfare indicators of the System of National Accounts 2008 (SNA) and improving their quality, including the quality of the consumption price indexes. Development of distributional indicators of income, consumption, and wealth should also be a priority. Definitions and assumptions can have big effects on these indicators and should be documented. Concerns have also arisen over potentially overlooked welfare growth from the emergence of the digital economy. However, the concern that free online platforms are missing from nominal GDP is incorrect. Also, many of the welfare effects of digitalization require complementary indicators, either because they are conceptually outside the boundary of GDP or impossible to quantify without making uncertain assumptions.
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.