Introduction: Big Data for Twenty-First-Century Economic Statistics: The Future Is Now
The measurement infrastructure for the production of economic statistics in the United States largely was established in the middle of the 20th century. The data landscape has changed in fundamental ways since this infrastructure was developed. Obtaining survey responses has become increasingly difficult, the economy has become more complex, and users are demanding ever more timely and granular data.
There is increasing interest in alternative sources of data that might allow the economic statistics agencies to better address users’ demands for information. Natively digital data have enormous potential for improving economic statistics. 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 could allow the statistical agencies to produce more accurate, more timely and more disaggregated statistics, with lower burden for data providers and perhaps at lower cost for the statistical agencies.
The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
I acknowledge financial support of the Alfred P. Sloan Foundation and the additional support of the Michigan Institute for Data Science and the
Michigan Institute for Teaching and Research in Economics.