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IMPROVING THE SYNTHETIC LONGITUDINAL BUSINESS DATABASE
February 2014
Working Paper Number:
CES-14-12
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments' confidentiality. Agencies potentially can manage these risks by releasing synthetic microdata, i.e., individual establishment records simulated from statistical models de- signed to mimic the joint distribution of the underlying observed data. Previously, we used this approach to generate a public-use version'now available for public use'of the U. S. Census Bureau's Longitudinal Business Database (LBD), a longitudinal cen- sus of establishments dating back to 1976. While the synthetic LBD has proven to be a useful product, we now seek to improve and expand it by using new synthesis models and adding features. This article describes our efforts to create the second generation of the SynLBD, including synthesis procedures that we believe could be replicated in other contexts.
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Environmental Regulation, Abatement, and Productivity: A Frontier Analysis
September 2013
Working Paper Number:
CES-13-51
This research studies the link between environmental regulation and plant level productivity in two U.S. manufacturing industries: pulp and paper mills and oil refineries using Data Envelopment Analysis (DEA) models. Data on abatement spending, emissions and abated emissions are used in different DEA models to study plant productivity outcomes when accounting for abatement spending or emissions regulations. Results indicate that pulp and paper mills and oil refineries in the U.S. suffered decreases in productivity due to pollution abatement activities from 1974 to 2000. These losses in productivity are substantial but have been slowly trending downwards even when the regulations have tended to become more stringent and emission of pollutants has declined suggesting that the best practice has shifted over time. Results also show that the reported abatement expenditures are not able to explain all the losses arising out of regulation suggesting that these abatement expenditures are consistently under-reported.
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Evaluating the Impact of MEP Services on Establishment Performance: A Preliminary Empirical Investigation
July 2012
Working Paper Number:
CES-12-15
This work examines the impact of manufacturing extension services on establishment productivity. It builds on an earlier study conducted by Jarmin in the 1990s, by matching the Census of Manufacturers (CMF) with the Manufacturing Extension Partnership (MEP) customer and activity datasets to generate treatment and comparison groups for analysis. The scope of the study is the period 1997 to 2002, which was a period of economic downturn in the manufacturing sector and budgetary challenges for the MEP. The paper presents some preliminary findings from this analysis. Both lagged dependent variable (LDV) and difference in difference (DiD) models are employed to estimate the relationship between manufacturing extension and labor productivity. The results presented are inconclusive and paint a mixed picture as they demonstrate the benefits and limitations of using Census microdata in program evaluation. They also point to the need to conduct analyses that could help to better understand the dynamic impact of MEP services.
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Issues and Challenges in Measuring Environmental Expenditures by U.S. Manufacturing: The Redevelopment of the PACE Survey
July 2007
Working Paper Number:
CES-07-20
The Pollution Abatement Costs and Expenditures (PACE) survey is the most comprehensive source of information on U.S. manufacturing's capital expenditures and operating costs associated with pollution abatement. In 2003, the U.S. Environmental Protection Agency began a significant initiative to redevelop the survey, guided by the advice of a multi-disciplinary workgroup consisting of economists, engineers, survey design experts, and experienced data users, in addition to incorporating feedback from key manufacturing industries. This paper describes some of these redevelopment efforts. Issues discussed include the approach to developing the new survey instrument, methods used to evaluate (and improve) its performance, innovations in sampling, and the special development and role of outside expertise. The completely redesigned PACE survey was first administered in early 2006.
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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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Efficiency Implications of Corporate Diversification: Evidence from Micro Data
November 2006
Working Paper Number:
CES-06-26
In this study, we contribute to the ongoing research on the rationales for corporate diversification. Using plant-level data from the U.S. Census Bureau, we examine whether combining several lines of business in one entity leads to increased productive efficiency. Studying the direct effect of diversification on efficiency allows us to discern between two major theories of corporate diversification: the synergy hypothesis and the agency cost hypothesis. To measure productive efficiency, we employ a non-parametric approach'a test based on Varian's Weak Axiom of Profit Maximization (WAPM). This method has several advantages over other conventional measures of productive efficiency. Most importantly, it allows one to perform the efficiency test without relying on assumptions about the functional form of the underlying production function. To the best of our knowledge, this study is the first application of the WAPM test to a large sample of non-financial firms. The study provides evidence that business segments of diversified firms are more efficient compared to single-segment firms in the same industry. This finding suggests that the existence of the so-called 'diversification discount' cannot be explained by efficiency differences between multi-segment and focused firms. Furthermore, more efficient segments tend to be vertically integrated with others segments in the same firm and to have been added through acquisitions rather than grown internally. Overall, the results of this study indicate that corporate diversification is value-enhancing, and that it is not necessarily driven by managers' pursuit of their private benefits.
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Decomposing Learning By Doing in New Plants
December 1992
Working Paper Number:
CES-92-16
The paper examines learning by doing in the context of a production function in which the other arguments are labor, human capital, physical capital, and vintage as a proxy for embodied technical change in physical capital. Learning is further decomposed into organization learning, capital learning, and manual task learning. The model is tested with time series and cross section data for various samples of up to 2,150 plants over a 14 year period. Word Perfect Version
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Technical Inefficiency And Productive Decline In The U.S. Interstate Natural Gas Pipeline Industry Under The Natural Gas Policy Act
October 1991
Working Paper Number:
CES-91-06
The U.S. natural gas industry has undergone substantial change since the enactment of the Natural Gas Policy Act of 1978. Although the major focus of the NGPA was to initiate partial and gradual price deregulation of natural gas at the well-head, the interstate transmission industry was profoundly affected by changes in the relative prices of competing fuels and contractual relationships among producers, transporters, distributors, and end-users. This paper assesses the impact of the NGPA on the technical efficiency and productivity of fourteen interstate natural gas transmission firms for the period 1978-1985. We focus on the distortionary effects that resulted in the industry during a period in which changes in regulatory policy could neither anticipate changing market conditions nor rapidly adjust to those changes. Two alternative estimating methodologies, stochastic frontier production analysis and data envelopment analysis, are used to measure the firm-specific and temporal distortionary effects. Concordant findings from these alternative methodologies suggest a pervasive pattern of declining technical efficiency in the industry during the period in which this major regulatory intervention was introduced and implemented. The representative firms experience an average annual decline in efficiency of .55 percent over the sample period. In addition, it appears that the industry suffered a decline in productivity during the sample period, averaging -1.18 percent annually.
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Measuring Total Factor Productivity, Technical Change And The Rate Of Returns To Research And Development
May 1991
Working Paper Number:
CES-91-03
Recent research indicates that estimates of the effect of research and development (R&D) on total factor productivity growth are sensitive to different measures of total factor productivity. In this paper, we use establishment level data for the flat glass industry extracted from the Census Bureau's Longitudinal Research Database (LRD) to construct three competing measures of total factor productivity. We then use these measures to estimate the conventional R&D intensity model. Our empirical results support previous finding that the estimated coefficients of the model are sensitive to the measurement of total factor productivity. Also, when using microdata and more detailed modeling, R&D is found to be a significant factor influencing productivity growth. Finally, for the flat glass industry, a specific technical change index capturing the learning-by-doing process appears to be superior to the conventional time trend index.
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