Multiple Classification Systems for Economic Data: Can a Thousand Flowers Bloom? And Should They?¹

Article type: Research Article

Authors: McGuckin, Robert H.^a

Affiliations: [a] Center for Economic Studies, U.S. Bureau of the Census, Washington, DC 20233-0001, USA

Note: [1] The opinions and conclusions expressed in this paper are those of the author and do not necessarily represent those of the U.S. Bureau of the Census. This paper was presented at the 1991 International Conference on the Classification of Economic Activity in Williamsburg, Virginia, November 6–8, 1991. Special thanks are due to Mark Doms, Timothy Dunne, John Haltiwanger, Sang Nguyen, and Jack Triplett for their comments and insights. Rebecca Turner provided excellent typing skills.

Abstract: The principle that the statistical system should provide flexibility – possibilities for generating multiple groupings of data to satisfy multiple objectives – if it is to satisfy users is universally accepted. Yet in practice, this goal has not been achieved. This paper discusses the feasibility of providing flexibility in the statistical system to accommodate multiple uses of the industrial data now primarily examined within the Standard Industrial Classification (SIC) system. In one sense, the question of feasibility is almost trivial. With today's computer technology, vast amounts of data can be manipulated and stored at very low cost. Reconfigurations of the basic data are very inexpensive compared to the cost of collecting the data. Flexibility in the statistical system implies more than the technical ability to regroup data. It requires that the basic data are sufficiently detailed to support user needs and are processed and maintained in a fashion that makes the use of a variety of aggregation rules possible. For this to happen, statistical agencies must recognize the need for high quality microdata and build this into their planning processes. Agencies need to view their missions from a multiple use perspective and move away from use of a primary reporting and collection vehicle. Although the categories used to report data must be flexible, practical considerations dictate that data collection proceed within a fixed classification system. It is simply too expensive for both respondents and statistical agencies to process survey responses in the absence of standardized forms, data entry programs, etc. I argue for a complete commodity classification – a list of commodities including materials and other intermediate products, services, raw materials, capital equipment, energy inputs, final products and labor by type – as the focus of data collection. The idea is to make the principle variables of interest – the commodities – the vehicle for the collection and processing of the data. For completeness, the basic classification should include labor usage through some form of occupational classification. In most economic surveys at the Census Bureau, the reporting unit and the classified unit have been the establishment. But there is no need for this to be so. The basic principle to be followed in data collection is that the data should be collected in the most efficient way – efficiency being defined jointly in terms of statistical agency collection costs and respondent burdens.

DOI: 10.3233/JEM-1993-19303

Journal: Journal of Economic and Social Measurement, vol. 19, no. 3, pp. 179-198, 1993