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Integrating Expenditure and Income Data: What to Do with the Statistical Discrepancy?

In: A New Architecture for the US National Accounts

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  • J. Joseph Beaulieu
  • Eric J. Bartelsman

Abstract

This discussion paper led to a publication in (D.W. Jorgenson, J.S. Landefeld, W.D. Nordhaus, eds.) 'A New Architecture for the U.S. National Accounts', NBER Studies in Income and Wealth , vol. 66, 309-54, University of Chicago Press, 2006. The purpose of this paper is to build consistent, integrated datasets to investigate whether various disaggregated data can shed light on the possible sources of the statistical discrepancy. Our strategy is first to use disaggregated data to estimate consistent sets of input-output models that sum to either GDP or GDI and compare the two in order to see where the discrepancy resides. We find a few “problem” industries that appear to explain most of the statistical discrepancy. Second, we explore what combination of the expenditure data and the income data seem to produce the most sensible data according to a few economic criteria. A mixture of data that do not aggregate either to GDP or to GDI appears optimal.
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Suggested Citation

  • J. Joseph Beaulieu & Eric J. Bartelsman, 2006. "Integrating Expenditure and Income Data: What to Do with the Statistical Discrepancy?," NBER Chapters, in: A New Architecture for the US National Accounts, pages 309-354, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:0141
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    Cited by:

    1. Baoline Chen, 2012. "A Balanced System of U.S. Industry Accounts and Distribution of the Aggregate Statistical Discrepancy by Industry," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 202-211, February.
    2. Carol Corrado & Paul Lengermann & Eric J. Bartelsman & J. Joseph Beaulieu, 2007. "Sectoral Productivity in the United States: Recent Developments and the Role of IT," German Economic Review, Verein für Socialpolitik, vol. 8(2), pages 188-210, May.
    3. Tommaso Fonzo & Marco Marini, 2015. "Reconciliation of systems of time series according to a growth rates preservation principle," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 651-669, November.
    4. Baoline Chen, 2006. "A Balanced System of Industry Accounts for the U.S. and Structural Distribution of Statistical Discrepancy," BEA Papers 0070, Bureau of Economic Analysis.
    5. Carol Corrado & Paul Lengermann & Eric J. Bartelsman & J. Joseph Beaulieu, 2007. "Sectoral Productivity in the United States: Recent Developments and the Role of IT," German Economic Review, Verein für Socialpolitik, vol. 8(2), pages 188-210, May.

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    More about this item

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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