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Suggested Citation:"References." National Research Council. 2006. Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11738.
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References

Foster, L., J. Elvery, R. Becker, C. Krizan, S. Nguyen, and D. Talan 2005 A Comparison for the Business Registers Used by the Bureau of Labor Statistics and the U.S. Census Bureau. Paper presented at the Joint Statistical Meetings, August 7, Minneapolis, MN.

Jorgenson, D., S. Landefeld, and W. Nordhaus 2006 A New Architecture for the U.S. National Accounts. Chicago: University of Chicago Press.

Norwood, J.L. 1995 Organizing to Count: Change in the Federal Statistical System. Washington, DC: Urban Institute Press.

Office of Management and Budget 2004 Statistical Programs of the United States Government, Fiscal Year 2004. Available: http://www.whitehouse.gov/omb/inforeg/04statprog.pdf [accessed May 2006].

Perry, G.L. 2005 Gauging employment: Is the professional wisdom wrong? Brookings Papers on Economic Activity 2:285-321.

Schmitt, J., and D. Baker 2006 Missing Inaction: Evidence of Undercounting of Non-Workers in the Current Population Survey. Washington, DC: Center for Economic and Policy Research.

U.S. Census Bureau 2002 North American Industry Classification System—United States 2002. Washington, DC: U.S. Department of Commerce.

U.S. Congress 2002 Confidential Information Protection and Statistical Efficiency Act of 2002. (HR 4528). Available: http://www.eia.doe.gov/oss/CIPSEA.pdf [accessed May 2006].

U.S. Department of the Treasury 1999 The Problem of Corporate Tax Shelters. Discussion, Analysis, and Legislative Proposals. (White paper, July.) Washington, DC: U.S. Department of the Treasury.

Suggested Citation:"References." National Research Council. 2006. Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11738.
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Suggested Citation:"References." National Research Council. 2006. Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11738.
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Page 43
Suggested Citation:"References." National Research Council. 2006. Improving Business Statistics Through Interagency Data Sharing: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11738.
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Page 44
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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.

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