U.S. business data are used broadly, providing the building blocks for key national—as well as regional and local—statistics measuring aggregate income and output, employment, investment, prices, and productivity. Beyond aggregate statistics, individual- and firm-level data are used for a wide range of microanalyses by academic researchers and by policy makers. In the United States, data collection and production efforts are conducted by a decentralized system of statistical agencies.1 This apparatus yields an extensive array of data that, particularly when made available in the form of microdata, provides an unparalleled resource for policy analysis and research on social issues and for the production of economic statistics. However, the decentralized nature of the statistical system also creates challenges to efficient data collection, to containment of respondent burden, and to maintaining consistency of terms and units of measurement. It is these challenges that raise to paramount importance the practice of effective data sharing among the statistical agencies.
During the workshop’s introductory session, Steven Landefeld— director of the Bureau of Economic Analysis (BEA), the workshop’s sponsoring agency—provided an overview of the goals motivating the event. He reflected on issues that arise in a decentralized statistical system, noting that its data products excel in detail, timeliness, and relevance but