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.

On cover & title page: ICD-10. For CD-ROM version see ISBN (9241545402). For Vol. 1 see (ISBN 9241546492); Vol. 2 see (ISBN 9241546530). This 2nd edition has not been mandated for use by the NHS

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.

Human capital—the knowledge, skills, and health that people accumulate over their lives—is a central driver of sustainable growth, poverty reduction, and successful societies. More human capital is associated with higher earnings for people, higher income for countries, and stronger cohesion in societies. Much of the hard-won human capital gains in many economies over the past decade is at risk of being eroded by the COVID-19 (coronavirus) pandemic. Urgent action is needed to protect these advances, particularly among the poor and vulnerable. Designing the needed interventions, targeting them to achieve the highest effectiveness, and navigating difficult trade-offs make investing in better measurement of human capital now more important than ever. The Human Capital Index (HCI)—launched in 2018 as part of the Human Capital Project—is an international metric that benchmarks the key components of human capital across economies. The HCI is a global effort to accelerate progress toward a world where all children can achieve their full potential. Measuring the human capital that children born today can expect to attain by their 18th birthdays, the HCI highlights how current health and education outcomes shape the productivity of the next generation of workers and underscores the importance of government and societal investments in human capital. The Human Capital Index 2020 Update: Human Capital in the Time of COVID-19 presents the first update of the HCI, using health and education data available as of March 2020. It documents new evidence on trends, examples of successes, and analytical work on the utilization of human capital. The new data—collected before the global onset of COVID-19—can act as a baseline to track its effects on health and education outcomes. The report highlights how better measurement is essential for policy makers to design effective interventions and target support. In the immediate term, investments in better measurement and data use will guide pandemic containment strategies and support for those who are most affected. In the medium term, better curation and use of administrative, survey, and identification data can guide policy choices in an environment of limited fiscal space and competing priorities. In the longer term, the hope is that economies will be able to do more than simply recover lost ground. Ambitious, evidence-driven policy measures in health, education, and social protection can pave the way for today’s children to surpass the human capital achievements and quality of life of the generations that preceded them.

By intelligence officials for intelligent people

A guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. In particular, this handbook is concerned with indicators which compare and rank country performance.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

The International Financial Statistics Yearbook, usually published in September, contains available annual data covering 12 years for countries appearing in the monthly issues of IFS. The IFS service is the standard source of international financial statistics. Additional time series in country tables and some additional tables of area and world aggregates are included in the Yearbook.

This is an update of OECD 2006 "Understanding National Accounts". It contains new data, new chapters and is adapted to the new systems of national accounts, SNA 2008 and ESA 2010.