The purpose of this book is to honour D.S. Prasada Rao and his many outstanding contributions to economic measurement, including index number methods for international comparisons of prices, real incomes, output, and productivity; stochastic approaches to index numbers; purchasing power parities for the measurement of regional and global inequality and poverty; and measurement of income and economic insecurity. This book brings together contributions by well-known and influential researchers in the field of economic measurement with special focus on topics in productivity measurement (Part I); income and health inequality, inequality of opportunity, and measurement of insecurity (Part II); index number theory and applications to consumer price index numbers, international comparisons of prices and real expenditures, and housing price index numbers (Part III). The chapters are authored by eminent researchers including Conchita D’Ambrosio, Bert Balk, Erwin Diewert, Robert Hill, Robert Inklaar, Knox Lovell, Robin Sickles, Jacques Silber and Marcel Timmer. The contributed papers offer in-depth reviews of the state of the art in these areas with a focus on the existing methods and applications, making the volume an invaluable source for both experienced researchers and new researchers, including PhD and other postgraduate students.

A bold agenda for a better way to assess societal well-being, by three of the world's leading economists and statisticians "If we want to put people first, we have to know what matters to them, what improves their well-being, and how we can supply more of whatever that is." —Joseph E. Stiglitz In 2009, a group of economists led by Nobel laureate Joseph E. Stiglitz, French economist Jean-Paul Fitoussi, and Nobel laureate Amartya Sen issued a report challenging gross domestic product (GDP) as a measure of progress and well-being. Published as Mismeasuring Our Lives by The New Press, the book sparked a global conversation about GDP and a major movement among scholars, policy makers, and activists to change the way we measure our economies. Now, in Measuring What Counts, Stiglitz, Fitoussi, and Martine Durand—summarizing the deliberations of a panel of experts on the measurement of economic performance and social progress hosted at the OECD, the international organization incorporating the most economically advanced countries—propose a new, "beyond GDP" agenda. This book provides an accessible overview of the last decade's global movement, sparked by the original critique of GDP, and proposes a new "dashboard" of metrics to assess a society's health, including measures of inequality and economic vulnerability, whether growth is environmentally sustainable, and how people feel about their lives. Essential reading for our time, it also serves as a guide for policy makers and others on how to use these new tools to fundamentally change the way we measure our lives—and to plot a radically new path forward.

Since the Great Depression, researchers and statisticians have recognized the need for more extensive methods for measuring economic growth and sustainability. The recent recession renewed commitments to closing long-standing gaps in economic measurement, including those related to sustainability and well-being. The latest in the NBER’s influential Studies in Income and Wealth series, which has played a key role in the development of national account statistics in the United States and other nations, this volume explores collaborative solutions between academics, policy researchers, and official statisticians to some of today’s most important economic measurement challenges. Contributors to this volume extend past research on the integration and extension of national accounts to establish an even more comprehensive understanding of the distribution of economic growth and its impact on well-being, including health, human capital, and the environment. The research contributions assess, among other topics, specific conceptual and empirical proposals for extending national accounts.

Astranger in academia cannot but be impressed by the apparent uniformity and precision of the methodology currently applied to the measurement of economic relationships. In scores of journal articles and other studies, a theoretical argument is typically presented to justify the position that a certain variable is related to certain other, possibly causal, variables. Regression or a related method is applied to a set of observations on these variables, and the conclusion often emerges that the causa,l variables are indeed "significant" at a certain "level," thereby lending support to the theoretical argument-an argument presumably formulated independently of the observations. A variable may be declared significant (and few doubt that this does not mean important) at, say, the 0. 05 level, but not the 0. 01. The effects of the variables are calculated to many significant digits, and are often accompanied by intervals and forecasts of not quite obvious meaning but certainly of reassuring "confidence. " The uniformity is also evident in the many mathematically advanced text books of statistics and econometrics, and in their less rigorous introductory versions for students in economics or business. It is reflected in the tools of the profession: computer programs, from the generaiones addressed to the incidental researcher to the dedicated and sophisticated programs used by the experts, display the same terms and implement the same methodology. In short, there appears no visible alternative to the established methodol ogy and no sign of reservat ions concerning its validity.

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.

Today's leading economists weigh in with a new "dashboard" of metrics for measuring our economic and social health "What we measure affects what we do. If we focus only on material well-being—on, say, the production of goods, rather than on health, education, and the environment—we become distorted in the same way that these measures are distorted." —Joseph E. Stiglitz A consensus has emerged among key experts that our conventional economic measures are out of sync with how most people live their lives. GDP, they argue, is a poor and outmoded measure of our well-being. The global movement to move beyond GDP has attracted some of the world's leading economists, statisticians, and social thinkers who have worked collectively to articulate new approaches to measuring economic well-being and social progress. In the decade since the 2008 economic crisis, these experts have come together to determine what indicators can actually tell us about people's lives. In the first book of its kind, leading economists from around the world, including Thomas Piketty, Emmanuel Saez, Elizabeth Beasely, Jacob Hacker, François Bourguignon, Nora Lustig, Alan B. Krueger, and Joseph E. Stiglitz, describe a range of fascinating metrics—from economic insecurity and environmental sustainability to inequality of opportunity and levels of trust and resilience—that can be used to supplement the simplistic measure of gross domestic product, providing a far more nuanced and accurate account of societal health and well-being. This groundbreaking volume is sure to provide a major source of ideas and inspiration for one of the most important intellectual movements of our time.