Because of the dramatic decline in the United States Trade Balance since the early 1970's, many economists and policy makers have become increasingly concerned about the ability of U.S. manufacturers to compete with foreign producers. Initially concern was limited to a few basic industries such as shoes, clothing, and steel; but more recently foreign producers have been effectively competing with U.S. manufacturers in automobiles, electronics, and other consumer products. It now seems that foreign producers are even challenging the dominance of America in high technology industries. The most recent publication from the International Trade Administration shows that the U.S. Trade Balance in high technology industries fell from a $24 billion surplus in 1982, to a $2.6 billion deficit in 1986, before rebounding to a $591 million surplus in 1987. As part of the efforts of the U.S. Census Bureau to provide policy makers and other interested parties with the most complete and accurate information possible, we recently completed a review of the methodology and data used to construct trade statistics in the area of high technology trade. Our findings suggest that the statistics presented by the International Trade Administration, although technically correct, do not provide an accurate picture of international trade in high or advanced technology products because of the level of aggregation used in their construction. The ITA statistics are based on the Department of Commerce's DOC3 definition of high technology industries. The DOC3 definition requires that each product classified in a high tech industry be designated high tech. As a result, many products which would not individually be considered high tech are included in the statistics. After developing a disaggregate, product- based measure of international trade in Advanced Technology Products (ATP), we find that although the trade balance in these products did decline over the 1982-1987 period, the decline is much smaller (about $5 billion) than reported by ITA (approximately $24 billion). This paper discusses the methodology used to define the ATP measure, contrasts it to the DOC3 measure, and provides a comparison of the resulting statistics. After discussing alternative approaches to identifying advanced technology products, Section 2 describes the advanced technologies in the classification. (Appendix A, provides definitions and examples of the products which embody these technologies. In addition, Appendix B, available on request, provides a comprehensive list of Advanced Technology Products by technology grouping.) Having described the ATPs, Section 3 examines annual trade statistics for ATP products, in 1982, 1986, and 1987, and compares these statistics with equivalent ones based on the DOC3 measure. The differences between the two measures over the 1982- 87 period stem from changes in the balance of trade of items included in the DOC3 measure but excluded by the Census ATP measure; i.e. the differences are due to changes in the trade balance of "low tech" products which are produced in "high tech" industries. This finding corroborates a principal argument for construction of the ATP measure, that the weakness of the DOC3 measure of high technology trade is the level of aggregation used in its construction. It also suggests that at the level of individual products the high technology sectors of the economy continue to enjoy a strong comparative advantage and are surprisingly healthy. Nonetheless, some areas of weakness are identified, such as low tech products in high tech industries. (Appendix C, supplements this material by providing a detailed listing of traded products included and excluded from the Advanced Technology definition for each DOC3 high tech commodity grouping. These Tables enable the reader to directly assess the Census classification.)
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The Classification of Manufacturing Industries: an Input-Based Clustering of Activity
August 1990
Working Paper Number:
CES-90-07
The classification and aggregation of manufacturing data is vital for the analysis and reporting of economic activity. Most organizations and researchers use the Standard Industrial Classification (SIC) system for this purpose. This is, however, not the only option. Our paper examines an alternative classification based on clustering activity using production technologies. While this approach yields results which are similar to the SIC, there are important differences between the two classifications in terms of the specific industrial categories and the amount of information lost through aggregation.
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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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Getting Patents and Economic Data to Speak to Each Other: An 'Algorithmic Links with Probabilities' Approach for Joint Analyses of Patenting and Economic Activity
September 2012
Working Paper Number:
CES-12-16
International technological diffusion is a key determinant of cross-country differences in economic performance. While patents can be a useful proxy for innovation and technological change and diffusion, fully exploiting patent data for such economic analyses requires patents to be tied to measures of economic activity. In this paper, we describe and explore a new algorithmic approach to constructing concordances between the International Patent Classification (IPC) system that organizes patents by technical features and industry classification systems that organize economic data, such as the Standard International Trade Classification (SITC), the International Standard Industrial Classification (ISIC) and the Harmonized System (HS). This 'Algorithmic Links with Probabilities' (ALP) approach incorporates text analysis software and keyword extraction programs and applies them to a comprehensive patent dataset. We compare the results of several ALP concordances to existing technology concordances. Based on these comparisons, we select a preferred ALP approach and discuss advantages of this approach relative to conventional approaches. We conclude with a discussion on some of the possible applications of the concordance and provide a sample analysis that uses our preferred ALP concordance to analyze international patent flows based on trade patterns.
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Concording U.S. Harmonized System Categories Over Time
May 2009
Working Paper Number:
CES-09-11
This paper: outlines an algorithm for concording U.S. ten-digit Harmonized System export and import codes over time; describes the concordances we construct for 1989 to 2004; and provides Stata code that can be used to construct similar concordances for arbitrary beginning and ending years from 1989 to 2007.
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An 'Algorithmic Links with Probabilities' Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition
March 2016
Working Paper Number:
CES-16-15
Patents are a useful proxy for innovation, technological change, and diffusion. However, fully exploiting patent data for economic analyses requires patents be tied to measures of economic activity, which has proven to be difficult. Recently, Lybbert and Zolas (2014) have constructed an International Patent Classification (IPC) to industry classification crosswalk using an 'Algorithmic Links with Probabilities' approach. In this paper, we utilize a similar approach and apply it to new patent classification schemes, the U.S. Patent Classification (USPC) system and Cooperative Patent Classification (CPC) system. The resulting USPC-Industry and CPC-Industry concordances link both U.S. and global patents to multiple vintages of the North American Industrial Classification System (NAICS), International Standard Industrial Classification (ISIC), Harmonized System (HS) and Standard International Trade Classification (SITC). We then use the crosswalk to highlight changes to industrial technology composition over time. We find suggestive evidence of strong persistence in the association between technologies and industries over time.
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Plant Exit and U.S. Imports from Low-Wage Countries
January 2016
Working Paper Number:
CES-16-02
Over the past twenty years, imports to the U.S. from low-wage countries have increased dramatically. In this paper we examine how low-wage country import competition in the U.S. influences the probability of manufacturing establishment closure. Confidential data from the U.S. Bureau of the Census are used to track all manufacturing establishments between 1992 and 2007. These data are linked to measures of import competition built from individual trade transactions. Controlling for a variety of plant and firm covariates, we show that low-wage import competition has played a significant role in manufacturing plant exit. Analysis employs fixed effects panel models running across three periods: the first plant-level panels examining trade and exit for the U.S. economy. Our results appear robust to concerns regarding endogeneity.
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Estimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
March 2007
Working Paper Number:
CES-07-07
A feature commonly used to distinguish between parametric/statistical models and engineering models is that engineering models explicitly represent best practice technologies while the parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are less useful in the corporate management of energy or for public policy goal setting. In the context of company or plant level energy management, it is more useful to have a measure of energy intensity capable of representing where a company or plant lies within a distribution of performance. In other words, is the performance close (or far) from the industry best practice? This paper presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or 'efficiency gap' at a plant, company or overall industry level. The approach requires plant level data and applies a stochastic frontier regression analysis to energy use. Stochastic frontier regression analysis separates the energy intensity into three components, systematic effects, inefficiency, and statistical (random) error. The stochastic frontier can be viewed as a sub-vector input distance function. One advantage of this approach is that physical product mix can be included in the distance function, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn refining plants.
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Primary Versus Secondary Production Techniques in U.S. Manufacturing
October 1994
Working Paper Number:
CES-94-12
In this paper we discuss and analyze a classical economic puzzle: whether differences in factor intensities reflect patterns of specialization or the co-existence of alternative techniques to produce output. We use observations on a large cross-section of U.S. manufacturing plants from the Census of Manufactures, including those that make goods primary to other industries, to study differences in production techniques. We find that in most cases material requirements do not depend on whether goods are made as primary products or as secondary products, which suggests that differences in factor intensities usually reflect patterns of specialization. A few cases where secondary production techniques do differ notably are discussed in more detail. However, overall the regression results support the neoclassical assumption that a single, best-practice technique is chosen for making each product.
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Recent Twists of the Wage Structure and Technology Diffusion
March 1994
Working Paper Number:
CES-94-05
This paper is an empirical study of the impact on U.S. wage structure of domestic technology, foreign technology, and import penetration. A model is presented which combines factor proportions theory with a version of growth theory. The model, which assumes two levels of skill, suggests that domestic technology raises both wages, while foreign technology, on a simple interpretation, lowers both. Trade at a constant technology, as usual, lowers the wage of that class of labor used intensively by the affected industry, and raises the other wage. The findings support the predictions of the model for domestic technology. On the other hand, they suggest that technological change, and perhaps other factors, have obscured the role of factor proportions in the data. Indeed, foreign technology and trade have the same effect on wages at different skill levels, not the opposite effects suggested by factor proportions. Finally, a simple diffusion story, in which foreign technology lowers all U.S. wages, is also rejected. Instead, uniformly higher U.S. wages, not lower, appear to be associated with the technology and trade of the oldest trading partners of the U.S., the economies of the West. Not so for Asia, especially the smaller countries which have recently accelerated their trade with the U.S. Their effects are uniformly negative on wages, suggesting a distinction between shock and long run effects of foreign technology and trade.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
CES-22-49
Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current
expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand.
First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation.
The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations.
Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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