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. 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. With this as the backdrop, the Bureau of Economic Analysis (BEA) asked the Committee on National Statistics of the National Academies to convene a workshop to discuss interagency business data sharing. The workshop was held October 21, 2005. This report is a summary of the discussions of that workshop. The workshop focused on the benefits of data sharing to two groups of stakeholders: the statistical agencies themselves and downstream data users. Presenters were asked to highlight untapped opportunities for productive data sharing that cannot yet be exploited because of regulatory or legislative constraints. The most prominently discussed example was that of tax data needed to reconcile the two primary business lists use by the statistical agencies.

The U.S. economy is highly dynamic: businesses open and close, workers switch jobs and start new enterprises, and innovative technologies redefine the workplace and enhance productivity. With globalization markets have also become more interconnected. Measuring business activity in this rapidly evolving environment increasingly requires tracking complex interactions among firms, establishments, employers, and employees. Understanding Business Dynamics presents strategies for improving the accuracy, timeliness, coverage, and integration of data that are used in constructing aggregate economic statistics, as well as in microlevel analyses of topics ranging from job creation and destruction and firm entry and exit to innovation and productivity. This book offers recommendations that could be enacted by federal statistical agencies to modernize the measurement of business dynamics, particularly the production of information on small and young firms that can have a disproportionately large impact in rapidly expanding economic sectors. It also outlines the need for effective coordination of existing survey and administrative data sources, which is essential to improving the depth and coverage of business data.

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. 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. With this as the backdrop, the Bureau of Economic Analysis (BEA) asked the Committee on National Statistics of the National Academies to convene a workshop to discuss interagency business data sharing. The workshop was held October 21, 2005. This report is a summary of the discussions of that workshop. The workshop focused on the benefits of data sharing to two groups of stakeholders: the statistical agencies themselves and downstream data users. Presenters were asked to highlight untapped opportunities for productive data sharing that cannot yet be exploited because of regulatory or legislative constraints. The most prominently discussed example was that of tax data needed to reconcile the two primary business lists use by the statistical agencies.

The U.S. Census Bureau maintains an important portfolio of economic statistics programs, including quinquennial economic censuses, annual economic surveys, and quarterly and monthly indicator surveys. Government, corporate, and academic users rely on the data to understand the complexity and dynamism of the U.S. economy. Historically, the Bureau's economic statistics programs developed sector by sector (e.g., separate surveys of manufacturing, retail trade, and wholesale trade), and they continue to operate largely independently. Consequently, inconsistencies in questionnaire content, sample and survey design, and survey operations make the data not only more difficult to use, but also more costly to collect and process and more burdensome to the business community than they could be. This report reviews the Census Bureau's annual economic surveys. Specifically, it examines the design, operations, and products of 11 surveys and makes recommendations to enable them to better answer questions about the evolving economy.

As digital technologies are expanding the power and reach of research, they are also raising complex issues. These include complications in ensuring the validity of research data; standards that do not keep pace with the high rate of innovation; restrictions on data sharing that reduce the ability of researchers to verify results and build on previous research; and huge increases in the amount of data being generated, creating severe challenges in preserving that data for long-term use. Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age examines the consequences of the changes affecting research data with respect to three issues - integrity, accessibility, and stewardship-and finds a need for a new approach to the design and the management of research projects. The report recommends that all researchers receive appropriate training in the management of research data, and calls on researchers to make all research data, methods, and other information underlying results publicly accessible in a timely manner. The book also sees the stewardship of research data as a critical long-term task for the research enterprise and its stakeholders. Individual researchers, research institutions, research sponsors, professional societies, and journals involved in scientific, engineering, and medical research will find this book an essential guide to the principles affecting research data in the digital age.

Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.