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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Price Dispersion in U.S. Manufacturing
October 1989
Working Paper Number:
CES-89-07
This paper addresses the question of whether products in the U.S. Manufacturing sector sell at a single (common) price, or whether prices vary across producers. The question of price dispersion is important for two reasons. First, if prices vary across producers, the standard method of using industry price deflators leads to errors in measuring real output at the firm or establishment level. These errors in turn lead to biased estimates of the production function and productivity growth equation as shown in Abbott (1988). Second, if prices vary across producers, it suggests that producers do not take prices as given but use price as a competitive variable. This has several implications for how economists model competitive behavior.
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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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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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Price Dispersion In U.S. Manufacturing: Implications For The Aggregation Of Products And Firms
March 1992
Working Paper Number:
CES-92-03
This paper addresses the question of whether products in the U.S. Manufacturing sector sell at a single (common) price, or whether prices vary across producers. Price dispersion is interesting for at least two reasons. First, if output prices vary across producers, standard methods of using industry price deflators lead to errors in measuring real output at the industry, firm, and establishment level which may bias estimates of the production function and productivity growth. Second, price dispersion suggests product heterogeneity which, if consumers do not have identical preferences, could lead to market segmentation and price in excess of marginal cost, thus making the current (competitive) characterization of the Manufacturing sector inappropriate and invalidating many empirical studies. In the course of examining these issues, the paper develops a robust measure of price dispersion as well as new quantitative methods for testing whether observed price differences are the result of differences in product quality. Our results indicate that price dispersion is widespread throughout manufacturing and that for at least one industry, Hydraulic Cement, it is not the result of differences in product quality.
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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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CONSTRUCTION OF REGIONAL INPUT-OUTPUT TABLES FROM ESTABLISHMENT-LEVEL MICRODATA: ILLINOIS, 1982
August 1993
Working Paper Number:
CES-93-12
This paper presents a new method for use in the construction of hybrid regional input-output tables, based primarily on individual returns from the Census of Manufactures. Using this method, input- output tables can be completed at a fraction of the cost and time involved in the completion of a full survey table. Special attention is paid to secondary production, a problem often ignored by input-output analysts. A new method to handle secondary production is presented. The method reallocates the amount of secondary production and its associated inputs, on an establishment basis, based on the assumption that the input structure for any given commodity is determined not by the industry in which the commodity was produced, but by the commodity itself -- the commodity-based technology assumption. A biproportional adjustment technique is used to perform the reallocations.
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Measuring Plant Level Energy Efficiency and Technical Change in the U.S. Metal-Based Durable Manufacturing Sector Using Stochastic Frontier Analysis
January 2016
Working Paper Number:
CES-16-52
This study analyzes the electric and thermal energy efficiency for five different metal-based durable manufacturing industries in the United States from 1987-2012 at the 3 digit North American Industry Classification System (NAICS) level. Using confidential plant-level data on energy use and production from the quinquennial U.S. Economic Census, a stochastic frontier regression analysis (SFA) is applied in six repeated cross sections for each five year census. The SFA controls for energy prices and climate-driven energy demand (heating degree days - HDD - and cooling degree days - CDD) due to differences in plant level locations, as well as 6-digit NAICS industry effects. A Malmquist index is used to decompose aggregate plant technical change in energy use into indices of efficiency and frontier (best practice) change. Own energy price elasticities range from -.7 to -1.0, with electricity tending to have slightly higher elasticity than fuel. Mean efficiency estimates (100 percent equals best practice level) range from a low of 32 percent (thermal 334 - Computer and Electronic Products) to a high of 86 percent (electricity 332 - Fabricated Metal Products). Electric efficiency is consistently better than thermal efficiency for all NAICS. There is no clear pattern to the decomposition of aggregate technical Thermal change. In some years efficiency improvement dominates; in other years aggregate technical change is driven by improvement in best practice.
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Grouped Variation in Factor Shares: An Application to Misallocation
August 2022
Working Paper Number:
CES-22-33
A striking feature of micro-level plant data is the presence of significant variation in factor cost shares across plants within an industry. We develop a methodology to decompose cost shares into idiosyncratic and group-specific components. In particular, we carry out a cluster analysis to recover the number and membership of groups using breaks in the dispersion of factor cost shares across plants. We apply our methodology to Chilean plant-level data and find that group-specific variation accounts for approximately one-third of the variation in factor shares across firms. We also study the implications ofthese groups in cost shares on the gains from eliminating misallocation. We place bounds on their importance and find that ignoring them can overstate the gains from eliminating misallocation by up to one-third.
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A General Inter-Industry Relatedness Index
December 2006
Working Paper Number:
CES-06-31
Firm growth and expansion is widely believed to be guided by the desire to leverage existing resources. But which resources? The answer depends largely on context.the peculiarities of industries, firms, technologies, production, customers, and a host of other dimensions. This fact makes pointing to any particular set of resources as the source of expansion decisions potentially problematic and makes more difficult tests of theories such as the resource-based view of the firm. This paper tackles the problem by developing a general inter-industry relatedness index that can be usefully applied across industry and firm contexts. The index harnesses the relatedness information embedded in the multi-product organization and diversification decisions of every firm in the US manufacturing economy. The index is general in that it implicitly varies the underlying resources upon which expansion proceeds with the industries in question and provides a percentile relatedness rank for every possible pair of fourdigit SIC manufacturing industries. The general index is tested for predictive validity and found to perform as expected. Applications of the index in strategy research are suggested.
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Estimating the Hidden Costs of Environmental Regulation
May 2002
Working Paper Number:
CES-02-10
This paper examines whether accounting systems identify all the costs of environmental regulation. We estimate the relation between the 'visible' cost of regulatory compliance, i.e., costs that are correctly classified in firms' accounting systems, and 'hidden' costs i.e., costs that are embedded in other accounts. We use plant-level data from 55 steel mills to estimate hidden costs, and we follow up with structured interviews of corporate-level managers and plant-level accountants. Empirical results show that a $1 increase in the visible cost of environmental regulation is associated with an increase in total cost (at the margin) of $10-11, of which $9-10 are hidden in other accounts. The findings suggest that inappropriate identification and accumulation of the costs of environmental compliance are likely to lead to distorted costs in firms subject to environmental regulation.
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