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Measuring the Electronic Economy: Current Status and Next Steps
June 2000
Working Paper Number:
CES-00-10
The recent growth of consumer retailing over the Internet draws attention to the electronic economy. However, businesses also conduct other business processes over computer networks, and many have been doing so for some time. Uses of computer networks attract attention because of assertions that they lead to new products and services, new delivery methods, streamlined or re-engineered business processes, new business structures, and enhanced business performance. These changes, in turn, potentially affect the performance of the entire economy, including economic growth, productivity, prices, employment, trade, and the structures of businesses, regions, and markets. Evaluating these assertions, and their effects on economic performance, requires solid statistical information about the electronic economy. This paper develops principles for identifying information critical to measuring the size and evaluating the potential effects of the electronic economy, relates that information to current data collection programs, and notes relevant measurement issues. Some of the required information about the electronic economy can be collected by adding questions to existing surveys, making the scope of existing surveys consistent, or developing new surveys. However, many key pieces of information pose significant challenges to economic measurement. While some of those challenges are specific to the electronic economy, others are long-standing ones. Interest in the electronic economy highlights the importance of continuing attempts to address these challenges. Improving and enhancing the statistical system to provide information about the electronic economy, therefore, would also substantially improve the baseline information available for evaluating the performance of the entire economy.
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THE MANUFACTURING PLANT OWNERSHIP CHANGE DATABASE: ITS CONSTRUCTION AND USEFULNESS
September 1998
Working Paper Number:
CES-98-16
The Center for Economic Studies, U. S. Bureau of the Census, has constructed the "Manufacturing Plant Ownership Change Database" (OCD)using plant-level data taken from the Census Bureau's Longitudinal Research Database (LRD). The OCD contains data on all manufacturing establishments that have experienced ownership change at least once during the period 1963-1992 . This is a unique data set which, together with the LRD, can be used to conduct a variety of economic studies that were not possible before. This paper describes how the OCD was constructed and discusses the usefulness of these data for economic research.
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The Census of Construction Industries Database
August 1998
Working Paper Number:
CES-98-10
The Census of Construction Industries (CCI) is conducted every five years as part of the quinquennial Economic Census. The Census of Construction Industries covers all establishments with payroll that are engaged primarily in contract construction or construction on their own account for sale as defined in the Standard Industrial Classification Manual. As previously administered, the CCI is a partial census including all multi-establishments and all establishments with payroll above $480,000, one out of every five establishments with payroll between $480,000 and $120,000 and one out of eight remaining establishments. The resulting database contains for each year approximately 200,000 establishments in the building construction, heavy construction and special trade construction industrial classifications. This paper compares the content, survey procedures, and sample response of the 1982, 1987 and 1992 Censuses of Construction.
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The Diffusion of Modern Manufacturing Practices: Evidence from Retail-Apparel Sectors
February 1997
Working Paper Number:
CES-97-11
As in many industries, firms in the apparel industry exhibit substantial heterogeneity in the adoption of "modern manufacturing" practices. Based on detailed business-unit level data, we show that this heterogeneity can be explained firm inputs. We show that the interaction between these explanatory factors means that complementarities between inputs may emerge over time rather than all at once as is often assumed in other studies of complementarities.
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Productivity Races II: The Issue of Capital Measurement
January 1997
Working Paper Number:
CES-97-03
This paper explores the role of capital measurement in determining the productivity of individual textile plants. In addition to gross book value of capital, we experiment with a perpetual inventory measure of capital and implicit (estimated) deflator associated with the age of the plant. Following the methodology of the earlier paper (Productivity Races I), we find that measures of productivity constructed from different measures of capital are highly correlated. Further, their association with alternative measures of economic performance is approximately the same. Nevertheless, the perpetual inventory measure of capital -- the most desirable measure from a theoretical perspective -- does consistently outperform the other two measures.
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Learning by Doing and Plant Characteristics
August 1996
Working Paper Number:
CES-96-05
Learning by doing, especially spillover learning, has received much attention lately in models of industry evolution and economic growth. The predictions of these models depend on the distribution of learning abilities and knowledge flows across firms and countries. However, the empirical literature provides little guidance on these issues. In this paper, I use plant level data on a sample of entrants in SIC 38, Instruments, to examine the characteristics associated with both proprietary and spillover learning by doing. The plant level data permit tests for the relative importance of within and between firm spillovers. I include both formal knowledge, obtained through R&D expenditures, and informal knowledge, obtained through learning by doing, in a production function framework. I allow the speed of learning to vary across plants according to characteristics such as R&D intensity, wages, and the skill mix. The results suggest that (a) Ainformal@ knowledge, accumulated through production experience at the plant, is a much more important source of productivity growth for these plants than is Aformal@ knowledge gained via research and development expenditures, (b) interfirm spillovers are stronger than intrafirm spillovers, (c) the slope of the own learning curve is positively related to worker quality, (d) the slope of the spillover learning curve is positively related to the skill mix at plants, (e) neither own nor spillover learning curve slopes are related to R&D intensities. These results imply that learning by doing may be, to some extent, an endogenous phenomenon at these plants. Thus, models of industry evolution that incorporate learning by doing may need to be revised. The results are also broadly consistent with the recent growth models.
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The Structure Of Production Technology Productivity And Aggregation Effects
August 1991
Working Paper Number:
CES-91-05
This is a sequel to an earlier paper by the author, Dhrymes (1990). Using the LRD sample, that paper examined the adequacy of the functional form specifications commonly employed in the literature of US Manufacturing production relations. The "universe" of the investigation was the three digit product group; the basic unit of observation was the plant; the sample consisted of all "large" plants, defined by the criterion that they employ 250 or more workers. The study encompassed three digit product groups in industries 35, 36 and 38, over the period 1972-1986, and reached one major conclusion: if one were to judge the adequacy of a given specification by the parametric compatibility of the estimates of the same parameters, as derived from the various implications of each specification, then the three most popular (production function) specifications, Cobb-Douglas, CES and Translog all fell very wide of the mark. The current paper focuses the investigation on two digit industries (but retains the plant as the basic unit of observation), i.e., our sample consists of all "large" manufacturing plants, in each of Industry 35, 36 and 38, over the period 1972-1986. It first replicates the approach of the earlier paper; the results are basically of the same genre, and for that reason are not reported herein. Second, it examines the extent to which increasing returns to scale characterize production at the two digit level; it is established that returns to scale at the mean, in the case of the translog production function are almost identical to those obtained with the Cobb-Douglas function.1 Finally, it examines the robustness and characteristics of measures of productivity, obtained in the context of an econometric formulation and those obtained by the method of what may be thought of as the "Solow Residual" and generally designated as Total Factor Productivity (TFP). The major finding here is that while there are some differences in productivity behavior as established by these two procedures, by far more important is the aggregation sensitivity of productivity measures. Thus, in the context of a pooled sample, introduction of time effects (generally thought to refer to productivity shifts) are of very marginal consequence. On the other hand, the introduction of four digit industry effects is of appreciable consequence, and this phenomenon is universal, i.e., it is present in industry 35, 36 as well as 38. The suggestion that aggregate productivity behavior may be largely, or partly, an aggregation phenomenon is certainly not a part of the established literature. Another persistent phenomenon uncovered is the extent to which productivity measures for individual plants are volatile, while two digit aggregate measures appear to be stable. These findings clearly calls for further investigation.
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Decomposing Technical Change
May 1991
Working Paper Number:
CES-91-04
A production function is specified with human capital as a separate argument and with embodied technical change proxied by a variable that measures the average vintage of the stock of capital. The coefficients of this production function are estimated with cross section data for roughly 2,150 new manufacturing plants in 41 industries, and for subsets of this sample. The question of interactions between new investment and initial endowments of capital is then examined with data for roughly 1,400 old plants in 15 industries.
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