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Testing the Advantages of Using Product Level Data to Create Linkages Across Industrial Coding Systems

Listed author(s):
  • Suzanne Peck
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    After the major revision of the U.S. Standard Industrial Classification system (SIC) in the 1987, the problem arose of how to evaluate industrial performance over time. The revision resulted in the creation of new industries, the combination of old industries, and the remixing of other industries to better reflect the present U.S. economy. A method had to be developed to make the old and new sets of industries comparable over time. Ryten (1991) argues for performing the conversion at the "most micro level," the product level. Linking industries should be accomplished by reclassifying product data of each establishment to a standard system, reassigning the primary activity of the establishment, reaggregating the data to the industry level, and then making the desired statistical comparison (Ryten, 1991). This paper discusses linking the data at the very micro, product level, and at the more macro, industry level. The results suggest that with complete product information the product level conversion is preferable for most industries in manufacturing because it recognizes that establishments may switch their primary industry because of the conversion. For some industries, especially those having no substantial changes in SIC codes over time, the conversion at the industry level is fairly accurate. A small group of industries lacks complete product information in 1982 to link the 1982 product codes to the 1987 codes. This results in having to rely on the industry concordance to create a time series of statistics.

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    Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 93-14.

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    Date of creation: Oct 1993
    Handle: RePEc:cen:wpaper:93-14
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    1. Robert H Mcguckin & Suzanne Peck, 1992. "Manufacturing Establishments Reclassified Into New Industries: The Effect Of Survey Design Rules," Working Papers 92-14, Center for Economic Studies, U.S. Census Bureau.
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