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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Manufacturing Establishments Reclassified Into New Industries: The Effect Of Survey Design Rules
November 1992
Working Paper Number:
CES-92-14
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.
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The Role of Industry Classification in the Estimation of Research and Development Expenditures
November 2014
Working Paper Number:
CES-14-45
This paper uses data from the National Science Foundation's surveys on business research and development (R&D) expenditures that have been linked with data from the Census Bureau's Longitudinal Business Database to produce consistent NAICS-based R&D time-series data based on the main product produced by the firm for 1976 to 2008.The results show that R&D spending has shifted away from domestic manufacturing industries in recent years. This is due in part to a shift in U.S. payrolls away from manufacturing establishments for R&D-performing firms.These findings support the notion of an increasingly fragmented production system for R&D-intensive manufacturing firms, whereby U.S. firms control output and provide intellectual property inputs in the form of R&D, but production takes place outside of the firms' U.S. establishments.
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Allocation of Company Research and Development Expenditures to Industries Using a Tobit Model
November 2015
Working Paper Number:
CES-15-42
This paper uses Census microdata and a regression-based approach to assign multi-division firms' pre-2008 Research and Development (R&D) expenditures to more than one industry. Since multi-division firms conduct R&D in more than one industry, assigning R&D to corresponding industries provides a more accurate representation of where R&D actually takes place and provides a consistent time-series with the National Science Foundation R&D by line of business information. Firm R&D is allocated to industries on the basis of observed industry payroll, as befits the historic importance of payroll in Census assignments of firms to industry. The results demonstrate that the method of assigning R&D to industries on the basis of payroll works well in earlier years, but becomes less effective over time as firms outsource their manufacturing function.
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A FIRST STEP TOWARDS A GERMAN SYNLBD: CONSTRUCTING A GERMAN LONGITUDINAL BUSINESS DATABASE
February 2014
Working Paper Number:
CES-14-13
One major criticism against the use of synthetic data has been that the efforts necessary to generate useful synthetic data are so in- tense that many statistical agencies cannot afford them. We argue many lessons in this evolving field have been learned in the early years of synthetic data generation, and can be used in the development of new synthetic data products, considerably reducing the required in- vestments. The final goal of the project described in this paper will be to evaluate whether synthetic data algorithms developed in the U.S. to generate a synthetic version of the Longitudinal Business Database (LBD) can easily be transferred to generate a similar data product for other countries. We construct a German data product with infor- mation comparable to the LBD - the German Longitudinal Business Database (GLBD) - that is generated from different administrative sources at the Institute for Employment Research, Germany. In a fu- ture step, the algorithms developed for the synthesis of the LBD will be applied to the GLBD. Extensive evaluations will illustrate whether the algorithms provide useful synthetic data without further adjustment. The ultimate goal of the project is to provide access to multiple synthetic datasets similar to the SynLBD at Cornell to enable comparative studies between countries. The Synthetic GLBD is a first step towards that goal.
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The Extent and Nature of Establishment Level Diversification in Sixteen U.S. Manufacturing Industries
August 1990
Working Paper Number:
CES-90-08
This paper examines the heterogeneity of establishments in sixteen manufacturing industries. Basic statistical measures are used to decompose product diversification at the establishment level into industry, firm, and establishment effects. The industry effect is the weakest; nearly all the observed heterogeneity is establishment specific. Product diversification at the establishment level is idiosyncratic to the firm. Establishments within a firm exhibit a significant degree of homogeneity, although the grouping of products differ across firms. With few exceptions, economies of scope and scale in production appear to play a minor role in the establishment's mix of outputs.
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Business Dynamics Statistics of High Tech Industries
January 2016
Working Paper Number:
CES-16-55
Modern market economies are characterized by the reallocation of resources from less productive, less valuable activities to more productive, more valuable ones. Businesses in the High Technology sector play a particularly important role in this reallocation by introducing new products and services that impact the entire economy. Tracking the performance of this sector is therefore of primary importance, especially in light of recent evidence that suggests a slowdown in business dynamism in High Tech industries. The Census Bureau produces the Business Dynamics Statistics (BDS), a suite of data products that track job creation, job destruction, startups, and exits by firm and establishment characteristics including sector, firm age, and firm size. In this paper we describe the methodologies used to produce a new extension to the BDS focused on businesses in High Technology industries.
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Multiple Classification Systems For Economic Data: Can A Thousand Flowers Bloom? And Should They?
December 1991
Working Paper Number:
CES-91-08
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 basic classification centered on commodities--products, services, raw materials and labor inputs--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.
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ARE FIXED EFFECTS FIXED? Persistence in Plant Level Productivity
May 1996
Working Paper Number:
CES-96-03
Estimates of production functions suffer from an omitted variable problem; plant quality is an omitted variable that is likely to be correlated with variable inputs. One approach is to capture differences in plant qualities through plant specific intercepts, i.e., to estimate a fixed effects model. For this technique to work, it is necessary that differences in plant quality are more or less fixed; if the "fixed effects" erode over time, such a procedure becomes problematic, especially when working with long panels. In this paper, a standard fixed effects model, extended to allow for serial correlation in the error term, is applied to a 16-year panel of textile plants. This parametric approach strongly accepts the hypothesis of fixed effects. They account for about one-third of the variation in productivity. A simple non-parametric approach, however, concludes that differences in plant qualities erode over time, that is plant qualities f-mix. Monte Carlo results demonstrate that this discrepancy comes from the parametric approach imposing an overly restrictive functional form on the data; if there were fixed effects of the magnitude measured, one would reject the hypothesis of f-mixing. For textiles, at least, the functional form of a fixed effects model appears to generate misleading conclusions. A more flexible functional form is estimated. The "fixed" effects actually have a half life of approximately 10 to 20 years, and they account for about one-half the variation in productivity.
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An Analysis of Key Differences in Micro Data: Results from the Business List Comparison Project
September 2008
Working Paper Number:
CES-08-28
The Bureau of Labor Statistics and the Bureau of the Census each maintain a business register, a universe of all U.S. business establishments and their characteristics, created from independent sources. Both registers serve critical functions such as supplying aggregate data inputs for certain national statistics generated by the Bureau of Economic Analysis. This paper examines key micro-level differences across these two business registers.
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RECOVERING THE ITEM-LEVEL EDIT AND IMPUTATION FLAGS IN THE 1977-1997 CENSUSES OF MANUFACTURES
September 2014
Working Paper Number:
CES-14-37
As part of processing the Census of Manufactures, the Census Bureau edits some data items and imputes for missing data and some data that is deemed erroneous. Until recently it was difficult for researchers using the plant-level microdata to determine which data items were changed or imputed during the editing and imputation process, because the edit/imputation processing flags were not available to researchers. This paper describes the process of reconstructing the edit/imputation flags for variables in the 1977, 1982, 1987, 1992, and 1997 Censuses of Manufactures using recently recovered Census Bureau files. Thepaper also reports summary statistics for the percentage of cases that are imputed for key variables. Excluding plants with fewer than 5 employees, imputation rates for several key variables range from 8% to 54% for the manufacturing sector as a whole, and from 1% to 72% at the 2-digit SIC industry level.
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