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Plant-Level Productivity and the Market Value of a Firm
June 2001
Working Paper Number:
CES-01-03
Some plants are more productive than others ' at least in terms of how productivity is conventionally measured. Do these differences represent an intangible asset? Does the stock market place a higher value on firms with highly productive plants? This paper tests this hypothesis with a new data set. We merge plant-level fundamental variables with firm-level financial variables. We find that firms with highly productive plants have higher market valuations as measured by Tobin's q ' productivity does indeed have a price.
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Exporting and Productivity
May 2000
Working Paper Number:
CES-00-07
Exporting is often touted as a way to increase economic growth. This paper examines whether exporting has played any role in increasing productivity growth in U.S. manufacturing. Contemporaneous levels of exports and productivity are indeed positively correlated across manufacturing industries. However, tests on industry data show causality from productivity to exporting but not the reverse. While exporting plants have substantially higher productivity levels, we find no evidence that exporting increases plant productivity growth rates. However, within the same industry, exporters do grow faster than non-exporters in terms of both shipments and employment. We show that exporting is associated with the reallocation of resources from less efficient to more efficient plants. In the aggregate, these reallocation effects are quite large, making up over 40 percent of total factor productivity growth in the manufacturing sector. Half of this reallocation to more productive plants occurs within industries and the direction of the reallocation is towards exporting plants. The positive contribution of exporters even shows up in import-competing industries and non-tradable sectors. The overall contribution of exporters to manufacturing productivity growth far exceeds their shares of employment and output.
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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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Are Some Firms Better at IT? Differing Relationships between Productivity and IT Spending
October 1999
Working Paper Number:
CES-99-13
Although recent studies have found a positive relationship between spending on information technology and firm productivity, the magnitude of this relationship has not been as dramatic as one would expect given the anecdotal evidence. Data collected by the Bureau of the Census is analyzed to investigate the relationship between plant-level productivity and spending on IT. This relationship is investigated by separating the manufacturing plants in the sample along two dimensions, total factor productivity and IT spending. Analysis along these dimensions reveals that there are significant differences between the highest and lowest productivity plants. The highest productivity plants tend to spend less on IT while the lowest productivity plants tend to spend more on IT. Although there is support for the idea that lower productivity plants are spending more on IT to compensate for their productivity shortcomings, the results indicate that this is not the only difference. The robustness of this finding is strengthened by investigating changes in productivity and IT spending over time. High productivity plants with the lowest amounts of IT spending tend to remain high productivity plants with low IT spending while low productivity plants with high IT spending tend to remain low productivity plants with high IT spending. The results show that management skill, as measured by the overall productivity level of a firm, is an additional factor that must be taken into consideration when investigating the IT "productivity paradox."
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IT Spending and Firm Productivity: Additional Evidence from the Manufacturing Sector
October 1999
Working Paper Number:
CES-99-10
The information systems (IS) "productivity paradox" is based on those studies that found little or no positive relationship between firm productivity and spending on IS. However, some earlier studies and one more recent study have found a positive relationship. Given the large amounts spent by organizations on information systems, it is important to understand the relationship between spending on IS and productivity. Beyond replicating positive results, an explanation is needed for the conflicting conclusions reached by these earlier studies. Data collected by the Bureau of the Census is analyzed to investigate the relationship between plant-level productivity and spending on IS. The relationship between productivity and spending on IS is investigated using assumptions and models similar to both studies with positive findings and studies with negative findings. First, the overall relationship is investigated across all manufacturing industries. Next, the relationship is investigated industry by industry. The analysis finds a positive relationship between plant-level productivity and spending on IS. The relationship is also shown to vary across industries. The conflicting results from earlier studies are explained by understanding the characteristics of the data analyzed in each study. A large enough sample size is needed to find the relatively smaller effect from IS spending as compared to other input spending included in the models. Because the relationship between productivity and IS spending varies across industries, industry mix is shown to be an important data characteristic that may have influenced prior results.
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Aggregate Productivity Growth: Lessons From Microeconomic Evidence
September 1998
Working Paper Number:
CES-98-12
In this study we focus on the role of the reallocation of activity across individual producers for aggregate productivity growth. A growing body of empirical analysis yields striking patterns in the behavior of establishment-level reallocation and productivity. Nevertheless, a review of existing studies yields a wide range of findings regarding the contribution of reallocation to aggregate productivity growth. Through our review of existing studies and our own sensitivity analysis, we find that reallocation plays a significant role in the changes in productivity growth at the industry level and that the impact of net entry is disproportionate since entering plants tend to displace less productive exiting plants, even after controlling for overall average growth in productivity. However, an important conclusion of our sensitivity analysis is that the quantitative contribution of reallocation to the aggregate change in productivity is sensitive to the decomposition methodology employed. Our findings also confirm and extend others in the literature that indicate that both learning and selection effects are important in this context. A novel aspect of our analysis is that we have examined the role of reallocation for aggregate productivity growth to a selected set of service sector industries. Our analysis considers the 4-digit industries that form the 3-digit industry automobile repair shops. We found tremendous churning in this industry with extremely large rates of entry and exit. Moreover, we found that productivity growth in the industry is dominated establishment data at Census, the results are quite striking and clearly call for further analysis.
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Manufacturing Extension And Productivity Dynamics
June 1998
Working Paper Number:
CES-98-08
This paper presents results from an investigation of the effects of manufacturing extension on the productivity dynamics of client plants. Previous econometric studies of manufacturing extension had very little time series information. This limited what researchers could say about the relative timing of extension services and performance improvements. In turn, this makes it difficult to attribute performance improvements to the receipt of extension services. In this paper, I use a panel of client and nonclient plants to more carefully analyze the dynamics of extension and productivity. The results suggest that the timing of observed productivity improvements at client plants is consistent with a positive impact of manufacturing extension. Estimated program impacts are within the range of those found in previous studies.
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Productivity Races II: The Issue of Capital Measurement
January 1997
Working Paper Number:
CES-97-03
This paper explores the role of capital measurement in determining the productivity of individual textile plants. In addition to gross book value of capital, we experiment with a perpetual inventory measure of capital and implicit (estimated) deflator associated with the age of the plant. Following the methodology of the earlier paper (Productivity Races I), we find that measures of productivity constructed from different measures of capital are highly correlated. Further, their association with alternative measures of economic performance is approximately the same. Nevertheless, the perpetual inventory measure of capital -- the most desirable measure from a theoretical perspective -- does consistently outperform the other two measures.
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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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Efficiency of Bankrupt Firms and Industry Conditions: Theory and Evidence
October 1996
Working Paper Number:
CES-96-12
We show that the incentives to reorganize inefficient firms and redeploy their assets depend on the change in industry output and industry characteristics. We use plant-level data to investigate the productivity of Chapter 11 bankrupt firms and asset-sale and closure decisions. We find no evidence of bankruptcy costs in industries with declining output growth, where most bankruptcies occur. In declining industries, bankrupt firms' plants are not less productive than industry averages and do not decline in productivity while in Chapter 11. In these industries, Chapter 11 appears to be a mechanism for fostering exit of capacity. In high-growth industries, there is some limited evidence of productivity declines while in Chapter 11 for a subsample of firms that remain in Chapter 11 for four or more years. Examining asset sales and closures by bankrupt firms and their competitors, we find that Chapter 11 status is of limited importance in predicting these decisions once industry and plant characteristics are taken into account. More generally, the findings imply that Chapter 11 may involve few real economic costs, and that industry effects and sample selection issues are very important in evaluating the performance of bankrupt firms.
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