Details

Subject(s)

• Economics—Statistical methods—Data processing [Browse]
• Big data [Browse]

Related name

• Conference on Research in Income and Wealth (2019 : Bethesda, Md.) [Browse]

Editor

• Abraham, Katharine G. [Browse]
• Jarmin, Ronald S., 1964- [Browse]
• Moyer, Brian [Browse]
• Shapiro, Matthew D. (Matthew David) [Browse]

Series

• Studies in income and wealth ; v. 79. [More in this series]
• Studies in income and wealth ; volume 79 [More in this series]

Summary note

"The measurement infrastructure for the production of economic statistics in the United States largely was established in the middle part of the 20th century. As has been noted by a number of commentators, the data landscape has changed in fundamental ways since this infrastructure was developed. Obtaining survey responses has become increasingly difficult, leading to increased data collection costs and raising concerns about the quality of the resulting data. At the same time, the economy has become more complex and users are demanding ever more timely and granular data. In this new environment, there is increasing interest in alternative sources of data that might allow the economic statistics agencies to better address users' demands for information. Recent years have seen a proliferation of natively digital data that have enormous potential for improving economic statistics. These include item-level transactional data on price and quantity from retail scanners or companies' inte rnal systems, credit card records, bank account records, payroll records and insurance records compiled for private business purposes; data automatically recorded by sensors or mobile devices; and a growing variety of data that can be obtained from websites and social media platforms. Staggering volumes of digital information relevant to measuring and understanding the economy are generated each second by an increasing array of devices that monitor transactions and business processes as well as track the activities of workers and consumers. Incorporating these non-designed Big Data sources into the economic measurement infrastructure holds the promise of allowing the statistical agencies to produce more accurate, more timely and more disaggregated statistics, with lower burden for data providers and perhaps even at lower cost for the statistical agencies. The agencies already have begun to make use of novel data to augment traditional data sources. Modern data science methods for using Big Data have advanced sufficiently to make the more systematic incorporation of these data into official statistics feasible. Indeed, the availability of new sources of data offers the opportunity to redesign the underlying architecture of official statistics. Considering the threats to the current measurement model arising from falling survey response rates, increased survey costs and the growing difficulties of keeping pace with a rapidly changing economy, fundamental changes in the architecture of the statistical system will be necessary to maintain the quality and utility of official statistics. This volume presents cutting edge research on the deployment of big data to solve both existing and novel challenges in economic measurement. The papers in this volume show that it is practical to incorporate big data into the production of economic statistics in real time and at scale. They report on the application of machine learning methods to extract usable new information from large ^volumes of data. They also lay out the challenges-both technical and operational-to using Big Data effectively in the production of economic statistics and suggest means of overcoming those challenges. Despite these challenges and the significant agenda for research and development they imply, the papers in the volume point strongly toward more systematic and comprehensive incorporation of Big Data to improve official economic statistics in the coming years"-- Provided by publisher.

Notes

"This volume contains revised versions of the papers presented at the Conference on Research in Income and Wealth entitled "Big Data for 21st Century Economic Statistics," held in Bethesda, MD, on March 15 - 16, 2019"--Page 14.

Bibliographic references

Includes bibliographical references and indexes.

Binding note

Copy 1. Binding: Includes dust-jacket.

Contents

• 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.

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Other title(s)

Big data for 21st century economic statistics

ISBN

• 9780226801254 ((hardcover))
• 022680125X ((hardcover))

LCCN

2021030585

OCLC

1259296816

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