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The Impact of Ownership Changes: A View from Labor Markets
March 2000
Working Paper Number:
CES-00-02
Previous studies of mergers and acquisition often focus on firms' performance such as profits, productivity and market shares. However, from a broad competition policy perspective, the impacts on labor and wages are crucial. In this study, we use plant-level data for the entire U.S. manufacturing for the period 1977-87 to examine the effects of ownership changes on employment, wages and plant closing. Our principal findings are that ownership changes are not a primary vehicle for cuts in employment and wages, or closing plants. Instead, the typical ownership change appear to increase jobs and their quality as measured by wages. However, some ownership changes, particularly those in bigger plants, are associated with job loss, and the typical worker fares much worse than the typical plant. Finally, we find that plants that changed owners have a higher probability of survival than those that did not. Overall, the impact of ownership changes on labor markets are positive.
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PRODUCTIVITY AND ACQUISITIONS IN U.S. COAL MINING
December 1999
Working Paper Number:
CES-99-17
This paper extends the literature on the productivity incentives for mergers and acquisitions. We develop a stochastic matching model that describes the conditions under which a coal mine will change owners. This model suggests two empirically testable hypotheses: i. acquired mines will exhibit low productivity prior to being acquired relative to non-acquired mines and ii. extant acquired mines will show post-acquisition productivity improvements over their pre-acquisition productivity levels. Using a unique micro data set on the universe of U.S. coal mines observed from 1978 to 1996, it is estimated that acquired coal mines are significantly less productive than non-acquired mines prior to having been acquired. Additionally, there is observable and significant evidence of post-acquisition productivity improvements. Finally, it is found that having been acquired positively and significantly influences the likelihood that a coal mine fails.
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Costs of Air Quality Regulation
July 1999
Working Paper Number:
CES-99-09
This paper explores some costs associated with environmental regulation. We focus on regulation pertaining to ground-level- ozone (O) and its effects on two manufacturing industries - industrial organic chemicals (SIC 2865-9) and miscellaneous plastic products (SIC 308). Both are major emitters of volatile organic compounds (VOC) and nitrogen oxides (NO), the chemical precursors to ozone. Using plant-level data from the Census Bureau's Longitudinal Research Database (LRD), we examine the effects of regulation on the timing and magnitudes of investments by firms and on the impact it has had on their operating costs. As an alternative way to assess costs, we also employ plant-level data from the Pollution Abatement Costs and Expenditures (PACE) survey. Analyses employing average total costs functions reveal that plants' production costs are indeed higher in (heavily-regulated) non-attainment areas relative to (less-regulated) attainment areas. This is particularly true for younger plants, consistent with the notion that regulation is most burdensome for new (rather existing) plants. Cost estimates using PACE data generally reveal lower costs. We also find that new heavily-regulated plants start out much larger than less-regulated plants, but then do not invest as much. Among other things, this highlights the substantial fixed costs involved in obtaining expansion permits. We also discuss reasons why plants may restrict their size.
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Large Plant Data in the LRD: Selection of a Sample for Estimation
March 1999
Working Paper Number:
CES-99-06
This paper describes preliminary work with the LRD during our tenure at the Census Bureau as participants in the ASA/NSF/Census Research Program. The objective of the work described here were two-fold. First, we wanted to examine the suitableness of these data for the calculation of plant-level productivity indexes, following procedures typically implemented with time series data. Second, we wanted to select a small number of 2-digit industry groups that would be well suited to the estimation of production functions and systems of factor share equations and factor demand forecasting equations with system-wide techniques. This description of our initial work may be useful to other researchers who are interested in the LRD for the analysis of productivity growth and/or the estimation of systems of factor equations, because the specific results reported in this memo suggest that the data are of good quality, or because the nature of the tasks undertaken provides insight into issues that arise in the analysis of longitudinal establishment data.
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Measuring The Performance Of Government Technology Programs: Lessons From Manufacturing Extension
December 1997
Working Paper Number:
CES-97-18
Managers of government technology programs are under increasing pressure to demonstrate the effectiveness of their programs. In this paper we examine the issues involved in credibly evaluating such programs in the context of recent efforts to evaluate manufacturing extension programs in the U.S. We provide a stylized model of the dynamic competitive environment in which the plants and firms targeted by these programs operate and discuss its implications for evaluation. We compare and contrast the various methodologies and data sets used to evaluate manufacturing extension. We conclude that the best currently available method for measuring the overall effectiveness of programs such as manufacturing extension is to combine program administrative data with existing panel data sets.
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Manufacturing Plant Location: Does State Pollution Regulation Matter?
July 1997
Working Paper Number:
CES-97-08
This paper tests whether differences across states in pollution regulation affect the location of manufacturing activity in the U.S. Plant-level data from the Census Bureau's Longitudinal Research Database is used to identify new plant births in each state over the 1963-1987 period. This is combined with several measures of state regulatory intensity, including business pollution abatement spending, regulatory enforcement activity, congressional pro-environment voting, and an index of state environmental laws. A significant connection is found: states with more stringent environmental regulation have fewer new manufacturing plants. These results persist across a variety of econometric specifications, and the strongest regulatory coefficients are similar in magnitude to thos4e on other factors expected to influence location, such as unionization rates. However, a subsample of high-pollution industries, which might have been expected to show much larger impacts, gets similar coefficients. This raises the possibility that differences between states other than environmental regulation might be influencing the results.
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Productivity Races I: Are Some Productivuty Measures Better Than Others?
January 1997
Working Paper Number:
CES-97-02
In this study we construct twelve different measures of productivity at the plant level and test which measures of productivity are most closely associated with direct measures of economic performance. We first examine how closely correlated these measures are with various measures of profits. We then evaluate the extent to which each productivity measure is associated with lower rates of plant closure and faster plant growth (growth in employment, output, and capital). All measures of productivity considered are credible in the sense that highly productive plants, regardless of measure, are clearly more profitable, less likely to close, and grow faster. Nevertheless, labor productivity and measures of total factor productivity that are based on regression estimates of production functions are better predictors of plant growth and survival than factor share-based measures of total factor productivity (TFP). Measures of productivity that are based on several years of data appear to outperform measures of productivity that are based solely on data from the most recent year.
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Measuring the Impact of the Manufacturing Extension Partnership
September 1996
Working Paper Number:
CES-96-08
In this paper, I measure the impact of the Manufacturing Extension Partnership (MEP) on productivity and sales growth at manufacturing plants. To do this, I match MEP client data to the Census Bureau's Longitudinal Research Database (LRD). The LRD contains data for all manufacturing establishments in the U.S. and provides a number of measures of plant performance and characteristics that are measured consistently across plants and time. This facilitates valid comparisons between both client and non-client plants and among clients served by different MEP centers. The National Institute of Standards and Technology (NIST) administers the MEP as part of their effort to improve the competitiveness of U.S. manufacturing. The program provides business and technical assistance to small and medium sized manufacturers much as agricultural extension does for farmers. The goal of the paper is to see if measures of plant performance (e.g., productivity and sales growth) are systematically related to participation in the MEP, while controlling for other factors that are known or thought to influence performance. Selection bias is often a problem in evaluation studies so I specify an econometric model that controls for selection. I estimate the model with data from 8 manufacturing extension centers in 2 states. The control group includes all plants from each state in the LRD. Preliminary results indicate that MEP participation is systematically related to productivity growth but not to sales growth.
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Learning by Doing and Plant Characteristics
August 1996
Working Paper Number:
CES-96-05
Learning by doing, especially spillover learning, has received much attention lately in models of industry evolution and economic growth. The predictions of these models depend on the distribution of learning abilities and knowledge flows across firms and countries. However, the empirical literature provides little guidance on these issues. In this paper, I use plant level data on a sample of entrants in SIC 38, Instruments, to examine the characteristics associated with both proprietary and spillover learning by doing. The plant level data permit tests for the relative importance of within and between firm spillovers. I include both formal knowledge, obtained through R&D expenditures, and informal knowledge, obtained through learning by doing, in a production function framework. I allow the speed of learning to vary across plants according to characteristics such as R&D intensity, wages, and the skill mix. The results suggest that (a) Ainformal@ knowledge, accumulated through production experience at the plant, is a much more important source of productivity growth for these plants than is Aformal@ knowledge gained via research and development expenditures, (b) interfirm spillovers are stronger than intrafirm spillovers, (c) the slope of the own learning curve is positively related to worker quality, (d) the slope of the spillover learning curve is positively related to the skill mix at plants, (e) neither own nor spillover learning curve slopes are related to R&D intensities. These results imply that learning by doing may be, to some extent, an endogenous phenomenon at these plants. Thus, models of industry evolution that incorporate learning by doing may need to be revised. The results are also broadly consistent with the recent growth models.
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Cross Sectional Variation In Toxic Waste Releases From The U.S. Chemical Industry
August 1994
Working Paper Number:
CES-94-08
This paper measures and examines the 1987 cross sectional variation in toxic releases from the U.S. chemical industry. The analysis is based on a unique plant level data set of over 2,100 plants, combining EPA toxic release data with Census Bureau data on economic activity. The main results are that intra-industry variation in toxic releases are as great as, or greater, than inter-industry variation, and that plant, firm, and regulatory characteristics are important factors in explaining observed variation in toxic releases. Even after controlling for primary product and plant characteristics, there are some firms that generate significantly lower toxic waste due to managerial ability and/or technology differences.
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