The objective of this study is to assess the possibility of differences in the production technologies between large and small establishments in five selected 4-digit SIC manufacturing industries. We particularly focus on estimating returns to scale and then make interferences regarding the efficiency of small businesses relative to large businesses. Using cross-section data for two census years, 1977 and 1982, we estimate a transcendental logarithmic (translog) production model that provides direct estimates of economies of scale parameters for both small and large establishments. Our primary findings are: (i) there are significant differences in the production technologies between small and large establishments; and (ii) based on the scale parameter estimates, small establishments appear to be as efficient as large establishments under normal economic conditions, suggesting that large size is not a necessary condition for efficient production. However, small establishments seem to be unable to maintain constant returns to scale production during economic recession such as that in 1982.
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Factor Substitution In U.S. Manufacturing: Does Plant Size Matter
April 1998
Working Paper Number:
CES-98-06
We use micro data for 10,412 U.S. manufacturing plants to estimate the degrees of factor substitution by industry and by plant size. We find that (1) capital, labor, energy and materials are substitutes in production, and (2) the degrees of substitution among inputs are quite similar across plant sizes in a majority of industries. Two important implications of these findings are that (1) small plants are typically as flexible as large plants in factor substitution; consequently, economic policies such energy conservation policies that result in rising energy prices would not cause negative effects on either large or small U.S. manufacturing plants; and (2) since energy and capital are found to be substitutes; the 1973 energy crisis is unlikely to be a significant factor contributing to the post 1973 productivity slowdown. of Substitution
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A Flexible Test for Agglomeration Economies in Two U.S. Manufacturing Industries
August 2004
Working Paper Number:
CES-04-14
This paper uses the inverse input demand function framework of Kim (1992) to test for economies of industry and urban size in two U.S. manufacturing sectors of differing technology intensity: farm and garden machinery (SIC 352) and measuring and controlling devices (SIC 382). The inverse input demand framework permits the estimation of the production function jointly with a set of cost shares without the imposition of prior economic restrictions. Tests using plant-level data suggest the presence of population scale (urbanization) economies in the moderate- to low-technology farm and garden machinery sector and industry scale (localization) economies in the higher technology measuring and controlling devices sector. The efficiency and generality of the inverse input demand approach are particularly appropriate for micro-level studies of agglomeration economies where prior assumptions regarding homogeneity and homotheticity are less appropriate.
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Capital-Energy Substitution Revisted: New Evidence From Micro Data
April 1997
Working Paper Number:
CES-97-04
We use new micro data for 11,520 plants taken from the Census Bureau=s 1991 Manufacturing Energy Consumption Survey (MECS) and 1991 Annual Survey of Manufactures (ASM) to estimate elasticities of substitution between energy and capital. We found that energy and capital are substitutes. We also found that estimates of Allen elasticities of substitution -- which have been used as a standard measure of substitution -- are sensitive to varying data sets and levels of aggregation. In contrast, estimates of Morishima elasticities of substitution -- which are theoretically superior to the Allen elasticities -- are more robust (except when two-digit level data are used). The results support the views that (i) the Morishima elasticity is a better measure of factor substitution and (ii) micro data provide more accurate elasticity estimates than those obtained from aggregate data. Our findings appear to resolve the long-standing conflict among the estimates reported in the many previous studies regarding energy-capital substitution/complementarity.
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The Influence Of Location On Productivity: Manufacturing Technology In Rural And Urban Areas
December 1991
Working Paper Number:
CES-91-10
Policies to counter the growing discrepancy between economic opportunities in rural and urban areas have focused predominantly on expanding manufacturing in rural areas. Fundamental to the design of these strategies are the relative costs of production and productivity of manufacturing in rural and urban areas. This study aims to develop information that can be used to assess the productivity of manufacturing in rural and urban areas. Production functions are estimated in the meat products and household furniture industries to investigate selected aspects of the effect of rural, small urban, and metropolitan location on productivity. The results show that the effect of location on productivity varies with industry, size, and the timing of the entry of the establishment into the industry. While the analysis is specific to two industries, it suggests that development policies targeting manufacturing can be made more effective by focusing on industries and plants with characteristics that predispose them to the locations being supported.
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Scale Economies and Consolidation in Hog Slaughter
March 2000
Working Paper Number:
CES-00-03
We use establishment based panel data to estimate a cost function which identifies the role of scale economies in hog slaughter consolidation. We find modest by extensive technological scale economies in the 1990s, and they became more important over time. But wages rose sharply with plant size through the 1970s and those wage premiums generated a pecuniary scale diseconomy that largely offset the effects of technological scale economies. The size-wage relation disappeared in the 1980; with growing technological scale economies and disappearing pecuniary diseconomies, large plants realized growing cost advantages over smaller plants, and production shifted to larger plants.
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An Empirical Analysis of Capacity Costs
January 2017
Working Paper Number:
CES-17-26
A central premise of management accounting is that including the cost of unused capacity in product costs can distort these costs and misguide users. Yet, there is little large-scale empirical evidence on the materiality of the cost of unused capacity. This study uses a confidential Census sample of 151,900 U.S. manufacturing plants from 1974-2011 to investigate the impact of separating the cost of unused capacity. We find that excluding the cost of unused capacity increases operating profit margins by approximately 26 percent. This order of magnitude is economically significant, and is pervasive across industries and over time. In additional analyses, we find that separating the cost of unused capacity largely smooths the time-series variation in unitized product costs and profit margins. Our finding of higher mean and lower variation of adjusted margins should be of considerable interest to both investors and managers.
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How Does Size Matter? Investigating the Relationships Among Plant Size, Industrial Structure, and Manufacturing Productivity
March 2011
Working Paper Number:
CES-11-08
Industrial concentration and market power have been studied extensively at the national scale, in fields ranging from economics and industrial organization to regional science and economic development. At the regional scale, however, industrial structure and firm size relationships have received little attention outside of non-generalizable case studies, primarily because accurate measurements require difficult-to-obtain plant- or firm-level information. Readily available secondary data sources on establishment size distributions (such as County Business Patterns or the Census of Manufactures) cannot be linked to performance information for particular establishments or firms. Yet region-specific industrial structure may be a crucial determinant of firm performance and thus regional economic fortunes as well (Chinitz 1961; Christopherson and Clark 2007). This paper examines how industrial concentration and agglomeration economies impact plant performance, focusing on the influence of establishment size in mediating these effects. The Longitudinal Research Database of the U.S. Census Bureau is accessed to construct production functions for three manufacturing industries nationwide. These production functions, specified at the establishment level, incorporate characteristics of establishments, industries, and regions, including spatially-differentiated measures of agglomeration economies. Establishment size is evaluated both as an absolute metric and relative to other regional industry plants, as theory suggests that absolute size may be most pertinent to agglomeration benefits but relative size more relevant to industrial structure (Caves and Barton 1990; Bothner 2005). The research builds on earlier work by the author that establishes a direct link between regional industry concentration and the productivity of manufacturing establishments.
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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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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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The Need to Account for Complex Sampling Features when Analyzing Establishment Survey Data: An Illustration using the 2013 Business Research and Development and Innovation Survey (BRDIS)
January 2017
Working Paper Number:
CES-17-62
The importance of correctly accounting for complex sampling features when generating finite population inferences based on complex sample survey data sets has now been clearly established in a variety of fields, including those in both statistical and non statistical domains. Unfortunately, recent studies of analytic error have suggested that many secondary analysts of survey data do not ultimately account for these sampling features when analyzing their data, for a variety of possible reasons (e.g., poor documentation, or a data producer may not provide the information in a publicuse data set). The research in this area has focused exclusively on analyses of household survey data, and individual respondents. No research to date has considered how analysts are approaching the data collected in establishment surveys, and whether published articles advancing science based on analyses of establishment behaviors and outcomes are correctly accounting for complex sampling features. This article presents alternative analyses of real data from the 2013 Business Research and Development and Innovation Survey (BRDIS), and shows that a failure to account for the complex design features of the sample underlying these data can lead to substantial differences in inferences about the target population of establishments for the BRDIS.
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