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Computers and Productivity: Are Aggregation Effects Important?

Author

Listed:
  • Robert H. McGuckin

    (The Conference Board)

  • Kevin Stiroh

    () (Federal Reserve Bank of New York (formerly with The Conference Board))

Abstract

This paper examines the empirical implications of aggregation bias when measuring the productive impact of computers. To isolate two specific aggregation problems relating to "aggregation in variables" and "aggregation in relations," we compare various production function estimates across a range of specifications, econometric estimators, and data levels. The results show that both sources of bias are important, especially as one moves from the sector to the economy level, and when the elasticity of all types of non-computer capital are incorrectly restricted to be equal. In terms of computers, however, the estimated elasticity is surprisingly stable between industry and sector regressions and does not appear to be biased by the incorporation of a restrictive measure of non-computer capital. The data consistently show that computers have a large impact on output.

Suggested Citation

  • Robert H. McGuckin & Kevin Stiroh, 2000. "Computers and Productivity: Are Aggregation Effects Important?," Economics Program Working Papers 00-03, The Conference Board, Economics Program.
  • Handle: RePEc:cnf:wpaper:0003
    as

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    File URL: http://www.conference-board.org/economics/workingpapers.cfm?pdf=E-0003-00-WP
    File Function: First version, 2000
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    References listed on IDEAS

    as
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    3. Franklin M. Fisher & John Monz (ed.), 1992. "Aggregation: Aggregate Production Functions and Related Topics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262561832, January.
    4. Dale W. Jorgenson & Kevin J. Stiroh, 2000. "Raising the Speed Limit: U.S. Economic Growth in the Information Age," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 31(1), pages 125-236.
    5. Jensen, J Bradford & McGuckin, Robert H, 1997. "Firm Performance and Evolution: Empirical Regularities in the US Microdata," Industrial and Corporate Change, Oxford University Press, pages 25-47.
    6. Surendra Gera & Wulong Wu & Frank C. Lee, 1999. "Information technology and productivity growth: an empirical analysis for Canada and the United States," Canadian Journal of Economics, Canadian Economics Association, vol. 32(2), pages 384-407, April.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
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    9. Charles Steindel, 1992. "Manufacturing productivity and high-tech investment," Quarterly Review, Federal Reserve Bank of New York, issue Sum, pages 39-47.
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    14. repec:fth:harver:1487 is not listed on IDEAS
    15. Stiroh, Kevin J, 1998. "Computers, Productivity, and Input Substitution," Economic Inquiry, Western Economic Association International, vol. 36(2), pages 175-191, April.
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    Cited by:

    1. Desmet, Klaus & Rossi-Hansberg, Esteban, 2009. "Spatial growth and industry age," Journal of Economic Theory, Elsevier, vol. 144(6), pages 2477-2502, November.
    2. Indjikian, Rouben & Siegel, Donald S., 2005. "The Impact of Investment in IT on Economic Performance: Implications for Developing Countries," World Development, Elsevier, vol. 33(5), pages 681-700, May.
    3. Duggal, Vijaya G. & Saltzman, Cynthia & Klein, Lawrence R., 2007. "Infrastructure and productivity: An extension to private infrastructure and it productivity," Journal of Econometrics, Elsevier, vol. 140(2), pages 485-502, October.

    More about this item

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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