The objective of this handbook is to be used as a reference in preparation of analysis of already available merchandise trade statistical information for assessment of various issues, discussion on negotiating positions and ultimately for conducting consultations. Indicators are grouped in the following categories: trade and economy, trade performance, direction of trade, sectoral structure of trade and protection.
Maps capture data expressing the economic complexity of countries from Albania to Zimbabwe, offering current economic measures and as well as a guide to achieving prosperity Why do some countries grow and others do not? The authors of The Atlas of Economic Complexity offer readers an explanation based on "Economic Complexity," a measure of a society's productive knowledge. Prosperous societies are those that have the knowledge to make a larger variety of more complex products. The Atlas of Economic Complexity attempts to measure the amount of productive knowledge countries hold and how they can move to accumulate more of it by making more complex products. Through the graphical representation of the "Product Space," the authors are able to identify each country's "adjacent possible," or potential new products, making it easier to find paths to economic diversification and growth. In addition, they argue that a country's economic complexity and its position in the product space are better predictors of economic growth than many other well-known development indicators, including measures of competitiveness, governance, finance, and schooling. Using innovative visualizations, the book locates each country in the product space, provides complexity and growth potential rankings for 128 countries, and offers individual country pages with detailed information about a country's current capabilities and its diversification options. The maps and visualizations included in the Atlas can be used to find more viable paths to greater productive knowledge and prosperity.
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.
The Manual sets out an internationally agreed framework for the compilation and reporting of statistics on international trade in services in the broad sense. It addresses the growing need, including in international trade negotiations and agreements, for more detailed, comparable, and comprehensive statistics on this type of trade in its various forms. The recommendations will enable countries to progressively expand and structure the information they compile in an internationally comparable way. The Manual conforms with and explicitly relates to the System of National Accounts 1993 and the fifth edition of the IMF’s Balance of Payments Manual. It is published jointly by the United Nations, European Union, IMF, OECD, UNCTAD, and World Trade Organization.
