Suchergebnisse

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.

Establishment reclassification occurs when an establishment classified in one industry in one year is reclassified into another industry in another year. Because of survey design rules at the Census Bureau these reclassifications occur systematically over time, and affect the industry-level time series of output and employment. The evidence shows that reclassified establishments occur most often in two distinct years over the life of a sample panel. Switches are not only numerous in these years, they also contribute significantly to measured industry change in industry output and employment. The problem is that reclassifications are not necessarily processed in the year that they occur. The survey rules restrict most change to certain years. The effect of these rules is evidenced by looking at the variance across industry growth rates which increases greatly in these two years. Whatever the reason for reclassifying an establishment, the way the switches are processed raises the possibility of measurement errors in the industry level statistics. Researchers and policymakers relying upon observations in annual changes in industry statistics should be aware of these systematic discontinuities, discrepancies and potential data distortions.

Official statistical agencies such as the Census Bureau and the Bureau of Labor Statistics collect enormous quantities of microdata in statistical surveys. These data are valuable for economic research and market and policy analysis. However, the data cannot be released to the public because of confidentiality commitments to individual respondents. These commitments, coupled with the strong research demand for microdata, have led the agencies to consider various proposals for releasing public use microdata. Most proposals for public use microdata call for the development of surrogate data that disguise the original data. Thus, they involve the addition of measurement errors to the data. In this paper, we examine disclosure issues and explore alternative masking methods for generating panels of useful economic microdata which can be released to researchers. While our analysis applies to all confidential microdata, applications using the Census Bureau's Longitudinal Research Data Base (LRD) are used for illustrative purposes throughout the discussion.

This paper provides an analysis of the trends in labour productivity and employment growth at industry level in the European Union and the United States during the 1990s. We analyse relationships for groups of industries, i.e. industries that produce ICT products and services, those that invest strongly in ICT, and those that make less intensive use of ICT. The main findings are that the inverse relationship between employment and productivity growth has been much more prominent in manufacturing industries than in services industries. Secondly, during the 1990s, this relationship has turned positive in many industries, in particular in ICT-producing industries and in ICT-using industries in the service sector. Finally, the employment-reducing effects of productivity growth have remained considerably stronger in Europe than in the US.