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Evidence on IO Technology Assumptions From the Longitudinal Research Database


  • Joe Mattey


This paper investigates whether a popular IO technology assumption, the commodity technology model, is appropriate for specific United States manufacturing industries, using data on product composition and use of intermediates by individual plants from the Census Longitudinal Research Database. Extant empirical research has suggested the rejection of this model, owing to the implication of aggregate data that negative inputs are required to make particular goods. The plant-level data explored here suggest that much of the rejection of the commodity technology model from aggregative data was spurious; problematic entries in industry-level IO tables generally have a very low Census content. However, among the other industries for which Census data on specified materials use is available, there is a sound statistical basis for rejecting the commodity technology model in about one-third of the cases: a novel econometric test demonstrates a fundamental heterogeneity of materials use among plants that only produce the primary products of the industry.

Suggested Citation

  • Joe Mattey, 1993. "Evidence on IO Technology Assumptions From the Longitudinal Research Database," Working Papers 93-8, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:93-8

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    References listed on IDEAS

    1. Timothy Dunne, 1991. "Technology Usage in U.S. Manufacturing Industries: New Evidence from the Survey of Manufacturing Technology," Working Papers 91-7, Center for Economic Studies, U.S. Census Bureau.
    2. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    3. Gollop, Frank M & Monahan, James L, 1991. "A Generalized Index of Diversification: Trends in U.S. Manufacturing," The Review of Economics and Statistics, MIT Press, vol. 73(2), pages 318-330, May.
    4. Steven J. Davis & John Haltiwanger, 1992. "Gross Job Creation, Gross Job Destruction, and Employment Reallocation," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 819-863.
    5. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    6. Coase, R H, 1992. "The Institutional Structure of Production," American Economic Review, American Economic Association, vol. 82(4), pages 713-719, September.
    7. McGuckin, Robert H & Nguyen, Sang V & Andrews, Stephen H, 1992. "The Relationships among Acquiring and Acquired Firms' Product Lines," Journal of Law and Economics, University of Chicago Press, vol. 34(2), pages 477-502, October.
    8. Edward Kokkelenberg & Sang Nguyen, 1989. "Modeling technical progress and total factor productivity: A plant level example," Journal of Productivity Analysis, Springer, vol. 1(1), pages 21-42, March.
    9. Robert H Mcguckin & Peter Zadrozny, 1988. "Long-Run Expectations And Capacity," Working Papers 88-1, Center for Economic Studies, U.S. Census Bureau.
    10. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    11. Newey, Whitney K., 1987. "Specification tests for distributional assumptions in the Tobit model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 125-145.
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    Cited by:

    1. Louis Mesnard, 2011. "Negatives in symmetric input–output tables: the impossible quest for the Holy Grail," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(2), pages 427-454, April.

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    CES; economic; research; micro; data; microdata; chief; economist;


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