TY - JOUR AU - Ehrlich, Gabriel AU - Haltiwanger, John AU - Jarmin, Ron AU - Johnson, David AU - Shapiro, Matthew D TI - Re-engineering Key National Economic Indicators JF - National Bureau of Economic Research Working Paper Series VL - No. 26116 PY - 2019 Y2 - July 2019 DO - 10.3386/w26116 UR - http://www.nber.org/papers/w26116 L1 - http://www.nber.org/papers/w26116.pdf N1 - Author contact info: Gabriel Ehrlich Department of Economics University of Michigan 611 Tappan St Ann Arbor, MI 48109-1220 E-Mail: gehrlich@umich.edu John C. Haltiwanger Department of Economics University of Maryland College Park, MD 20742 Tel: 301/405-3504 Fax: 301/405-3542 E-Mail: haltiwan@econ.umd.edu Ron S. Jarmin U.S. Census Bureau 4600 Silver Hill Road Washington, DC 20233 Tel: 301.763.1858 E-Mail: ron.s.jarmin@census.gov David Johnson ISR 426 Thompson, Rm 3234 Ann Arbor, MI 48106 United States Tel: 5713296759 E-Mail: johnsods@umich.edu Matthew D. Shapiro Department of Economics University of Michigan 611 Tappan St Ann Arbor, MI 48109-1220 Tel: 734/764-5419 Fax: 734 764-2769 E-Mail: shapiro@umich.edu M1 - published as Gabriel Ehrlich, John Haltiwanger, Ron Jarmin, David Johnson, Matthew D. Shapiro. "Re-Engineering Key National Economic Indicators," in Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro, "Big Data for 21st Century Economic Statistics" University of Chicago Press (2020) M3 - presented at "CRIW Conference: Big Data for 21st Century Economic Statistics", March 15-16, 2019 AB - Traditional methods of collecting data from businesses and households face increasing challenges. These include declining response rates to surveys, increasing costs to traditional modes of data collection, and the difficulty of keeping pace with rapid changes in the economy. The digitization of virtually all market transactions offers the potential for re-engineering key national economic indicators. The challenge for the statistical system is how to operate in this data-rich environment. This paper focuses on the opportunities for collecting item-level data at the source and constructing key indicators using measurement methods consistent with such a data infrastructure. Ubiquitous digitization of transactions allows price and quantity be collected or aggregated simultaneously at the source. This new architecture for economic statistics creates challenges arising from the rapid change in items sold. The paper explores some recently proposed techniques for estimating price and quantity indices in large-scale item-level data. Although those methods display tremendous promise, substantially more research is necessary before they will be ready to serve as the basis for the official economic statistics. Finally, the paper addresses implications for building national statistics from transactions for data collection and for the capabilities and organization of the statistical agencies in the 21st century. ER -