CREAT: Census Research Exploration and Analysis Tool

Evidence on IO Technology Assumptions From the Longitudinal Research Database

May 1993

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The 10 most similar working papers to the working paper 'Evidence on IO Technology Assumptions From the Longitudinal Research Database' are listed below in order of similarity.

• Working Paper

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  Primary Versus Secondary Production Techniques in U.S. Manufacturing
  

  October 1994

  Authors: Joe Mattey, Thijs T Raa

  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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• Working Paper

  
  
  Price Dispersion in U.S. Manufacturing
  

  October 1989

  Authors: Thomas A Abbott Iii

  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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• Working Paper

  
  
  Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy
  

  August 2018

  Authors: Reed Walker, Joseph S. Shapiro, Eva Lyubich

  Working Paper Number:

  CES-18-03R

  This paper provides the first estimates of within-industry heterogeneity in energy and CO2 productivity for the entire U.S. manufacturing sector. We measure energy and CO2 productivity as output per dollar energy input or per ton CO2 emitted. Three findings emerge. First, within narrowly defined industries, heterogeneity in energy and CO2 productivity across plants is enormous. Second, heterogeneity in energy and CO2 productivity exceeds heterogeneity in most other productivity measures, like labor or total factor productivity. Third, heterogeneity in energy and CO2 productivity has important implications for environmental policies targeting industries rather than plants, including technology standards and carbon border adjustments.
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• Working Paper

  
  
  Multiple Classification Systems For Economic Data: Can A Thousand Flowers Bloom? And Should They?
  

  December 1991

  Authors: Robert H Mcguckin

  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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• Working Paper

  
  
  Materials Prices and Productivity
  

  June 2012

  Authors: Enghin Atalay

  Working Paper Number:

  CES-12-11

  There is substantial within-industry variation, even within industries that use and produce homogeneous inputs and outputs, in the prices that plants pay for their material inputs. I explore, using plant-level data from the U.S. Census Bureau, the consequences and sources of this variation in materials prices. For a sample of industries with relatively homogeneous products, the standard deviation of plant-level productivities would be 7% lower if all plants faced the same materials prices. Moreover, plant-level materials prices are both persistent across time and predictive of exit. The contribution of net entry to aggregate productivity growth is smaller for productivity measures that strip out di'erences in materials prices. After documenting these patterns, I discuss three potential sources of materials price variation: geography, di'erences in suppliers. marginal costs, and suppliers. price discriminatory behavior. Together, these variables account for 13% of the dispersion of materials prices. Finally, I demonstrate that plants.marginal costs are correlated with the marginal costs of their intermediate input suppliers.
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• Working Paper

  
  
  The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing
  

  June 2020

  Authors: Gale Boyd, Matthew Doolin

  Working Paper Number:

  CES-20-18

  This paper estimates stochastic frontier energy demand functions with non-public, plant-level data from the U.S. Census Bureau to measure the energy efficiency gap and energy price elasticities in the food processing industry. The estimates are for electricity and fuel use in 4 food processing sectors, based on the disaggregation of this industry used by the National Energy Modeling System Industrial Demand Module. The estimated demand functions control for plant inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. Own price elasticities range from 0.6 to -0.9 with little evidence of fuel/electricity substitution. The magnitude of the efficiency estimates is sensitive to the assumptions but consistently reveal that few plants achieve 100% efficiency. Defining a 'practical level of energy efficiency' as the 95th percentile of the efficiency distributions and averaging across all the models result in a ~20% efficiency gap. However, most of the potential reductions in energy use from closing this efficiency gap are from plants that are 'low hanging fruit'; 13% of the 20% potential reduction in the efficiency gap can be obtained by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industry; ranging from a low of 0.4% to a high of 5.7%.
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• Working Paper

  
  
  Output Market Segmentation and Productivity
  

  June 2001

  Authors: Chad Syverson

  Working Paper Number:

  CES-01-07

  Recent empirical investigations have shown enormous plant-level productivity heterogeneity, even within narrowly defined industries. Most of the theoretical explanations for this have focused on factors that influence the production process, such as idiosyncratic technology shocks or input price differences. I claim that characteristics of the output demand markets can also have predictable influences on the plant-level productivity distribution within an industry. Specifically, an industry's degree of output market segmentation (i.e., the substitutability of one plant's output for another's in that industry) should impact the dispersion and central tendency of the industry's plant-level productivity distribution. I test this notion empirically by seeing if measurable cross-sectional variation in market segmentation affects moments of industry's plant-level productivity distribution moments. I find significant and robust evidence consistent with this notion.
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• Working Paper

  
  
  Estimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
  

  March 2007

  Authors: Gale Boyd

  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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• Working Paper

  
  
  Exploring New Ways to Classify Industries for Energy Analysis and Modeling
  

  November 2022

  Authors: Gale Boyd, Matthew Doolin, Liz Wachs, Colin McMillan

  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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• Working Paper

  
  
  The Extent and Nature of Establishment Level Diversification in Sixteen U.S. Manufacturing Industries
  

  August 1990

  Authors: Mary L Streitwieser

  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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