An NBER conference on Big Data for 21st Century Economic Statistics met in Washington on March 15–16. Research Associates Katharine G. Abraham of the University of Maryland and Matthew D. Shapiro of the University of Michigan; Ron S. Jarmin of the U.S. Census Bureau; and Brian Moyer of the Bureau of Economic Analysis organized the meeting, which was sponsored by the Alfred P. Sloan Foundation. These researchers' papers were presented and discussed:
Carol Robbins, National Science Foundation; Jose Bayoan Santiago Calderon, Claremont Graduate University; Gizem Korkmaz, Daniel Chen, Sallie Keller, Aaron Schroeder, and Stephanie S. Shipp, University of Virginia; Claire Kelling, Pennsylvania State University, "The Scope and Impact of Open Source Software as Intangible Capital: A Framework for Measurement with an Application Based on the Use of R and Python Packages"
Katharine G. Abraham, University of Maryland and NBER; Margaret Levenstein, University of Michigan; and Matthew D. Shapiro, University of Michigan and NBER, "Securing Commercial Data for Economic Statistics"
W. Erwin Diewert, University of British Columbia and NBER, and Robert C. Feenstra, University of California, Davis and NBER, "Estimating the Benefits of New Products"
David Copple, Bradley J. Speigner, and Arthur Turrell, Bank of England, "Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings"
Edward L. Glaeser, Harvard University and NBER, and Hyunjin Kim and Michael Luca, Harvard University, "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity" (NBER Working Paper No. 24010)
Rishab Guha, Harvard University, and Serena Ng, Columbia University and NBER, "A Machine-Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data"
Gabriel Ehrlich and David Johnson, University of Michigan; John C. Haltiwanger, University of Maryland and NBER; Ron S. Jarmin, U.S. Census Bureau; and Matthew D. Shapiro, University of Michigan and NBER, "Re-Engineering Key National Economic Indicators"
Andrea Batch, Jeffrey C. Chen, Alexander Driessen, Abe Dunn, and Kyle K. Hood, Bureau of Economic Analysis, "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators"
Tomaz Cajner, Leland D. Crane, Ryan Decker, Adrian Hamins-Puertolas, and Christopher Kurz, Federal Reserve Board, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data"
J. Bradford Jensen, Georgetown University and NBER; Shawn D. Klimek, Andrew L. Baer, and Joseph Staudt, U.S. Census Bureau; and Lisa Singh and Yifang Wei, Georgetown University, "Automating Response Evaluation for Franchising Questions on the 2017 Economic Census"
Sudip Bhattacharjee and Ugochukwu Etudo, University of Connecticut, and John Cuffe, Justin Smith, and Nevada Basdeo, U.S. Census Bureau, "Using Public Data to Generate Industrial Classification Codes"
Jeremy Moulton, University of North Carolina, Chapel Hill, and Marina Gindelsky and Scott A. Wentland, Bureau of Economic Analysis, "Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveraging Zillow Microdata"
Shifrah Aron-Dine, Stanford University, and Aditya Aladangady, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia R. Sahm, Federal Reserve Board, "From Transactions Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending"
David Friedman, Crystal G. Konny, and Brendan K. Williams, Bureau of Labor Statistics, "Big Data in the U.S. Consumer Price Index: Experiences & Plans"
Don Fast and Susan Fleck, Bureau of Labor Statistics, "Measuring Export Price Movements with Administrative Trade Data"
Rebecca J. Hutchinson, U.S. Census Bureau, "Investigating Alternative Data Sources to Reduce Respondent Burden in United States Census Bureau Retail Economic Data Products"
Abe Dunn, Bureau of Economic Analysis; Dana Goldman and Neeraj Sood, University of Southern California and NBER; and John Romley, University of Southern California, "Quantifying Productivity Growth in Health Care Using Insurance Claims and Administrative Data"
Summaries of these papers are at www.nber.org/conferences/2019/CRIWs19/summary.html