Big data for twenty-first-century economic statistics
Author(s)
Bibliographic Information
Big data for twenty-first-century economic statistics
（Studies in income and wealth, v. 79）
University of Chicago Press, 2022
- : cloth
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Big data for 21st century economic statistics
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Note
"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 Washington, DC, on March 15-16, 2019"--Prefatory note
Includes bibliographical references and indexes
Description and Table of Contents
Description
The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement.
The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data—such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers—has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.
Table of Contents
Prefatory Note
Introduction: Big Data for Twenty- First- Century Economic Statistics: The Future Is Now
Katherine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro
I. TOWARD COMPREHENSIVE USE OF BIG DATA IN ECONOMIC STATISTICS
1. Reengineering Key National Economic Indicators
Gabriel Ehrlich, John C. Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro
2. Big Data in the US Consumer Price Index: Experiences and Plans
Crystal G. Konny, Brendan K. Williams, and David M. Friedman
3. Improving Retail Trade Data Products Using Alternative Data Sources
Rebecca J. Hutchinson
4. 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
5. 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
II. USES OF BIG DATA FOR CLASSIFICATION
6. 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
7. 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
8. 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
III. USES OF BIG DATA FOR SECTORAL MEASUREMENT
9. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity
Edward L. Glaeser, Hyunjin Kim, and Michael Luca
10. Unit Values for Import and Export Price Indexes: A Proof of Concept
Don A. Fast and Susan E. Fleck
11. 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
12. 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
IV. METHODOLOGICAL CHALLENGES AND ADVANCES
13. 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
14. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data
Rishab Guha and Serena Ng
15. Estimating the Benefits of New Products
W. Erwin Diewert and Robert C. Feenstra
Contributors
Author Index
Subject Index
by "Nielsen BookData"