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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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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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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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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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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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Whittling Away At Productivity Dispersion Futher Notes: Persistent Dispersion or Measurement Error?
November 1996
Working Paper Number:
CES-96-11
This note considers several hypotheses regarding measurement error as a source of observed cross-sectional dispersion in plant-level productivity in the US textile industry. The hypotheses that reporting error and/or price rigidity in either materials and/or output account for a substantial portion of the observed dispersion in productivity are consistent with the data. Similarly, the hypothesis that transitory product niches or fashion effects lead to differential markups and consequently dispersion in observed productivity is consistent with the data. The hypothesis that transfer pricing problems lead to persistent differences in plant-level productivity, in contrast, does not appear to be consistent with the data. Finally, the hypothesis that some plants have permanent product niches that lead to dispersion in observed productivity does not appear to be consistent with data. In order to avoid imposing a strong functional form on the data, this note follows a non-parametric methodology developed in the early paper.
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ARE FIXED EFFECTS FIXED? Persistence in Plant Level Productivity
May 1996
Working Paper Number:
CES-96-03
Estimates of production functions suffer from an omitted variable problem; plant quality is an omitted variable that is likely to be correlated with variable inputs. One approach is to capture differences in plant qualities through plant specific intercepts, i.e., to estimate a fixed effects model. For this technique to work, it is necessary that differences in plant quality are more or less fixed; if the "fixed effects" erode over time, such a procedure becomes problematic, especially when working with long panels. In this paper, a standard fixed effects model, extended to allow for serial correlation in the error term, is applied to a 16-year panel of textile plants. This parametric approach strongly accepts the hypothesis of fixed effects. They account for about one-third of the variation in productivity. A simple non-parametric approach, however, concludes that differences in plant qualities erode over time, that is plant qualities f-mix. Monte Carlo results demonstrate that this discrepancy comes from the parametric approach imposing an overly restrictive functional form on the data; if there were fixed effects of the magnitude measured, one would reject the hypothesis of f-mixing. For textiles, at least, the functional form of a fixed effects model appears to generate misleading conclusions. A more flexible functional form is estimated. The "fixed" effects actually have a half life of approximately 10 to 20 years, and they account for about one-half the variation in productivity.
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The Effect Of Technology Use On Productivity Growth
April 1996
Working Paper Number:
CES-96-02
This paper examines the relationship between the use of advanced technologies and productivity and productivity growth rates. We use data from the 1993 and 1988 Survey of Manufacturing Technology (SMT) to examine the use of advanced (computer based) technologies at two different points in time. We are also able to combine the survey data with the Longitudinal Research Database (LRD) to examine the relationships between plant performance, plant characteristics, and the use of advanced technologies. In addition, a subset of these plants were surveyed in both years, enabling us to directly associate changes in technology use with changes in plant productivity performance. The main findings of the study are as follows. First, diffusion is not the same across the surveyed technologies. Second, the adoption process is not smooth: plants added and dropped technologies over the six-year interval 1988-93. In fact, the average plant showed a gross change of roughly four technologies in achieving an average net increase of less than one new technology. In this regard, technology appears to be an experience good: plants experiment with particular technologies before deciding to add additional units or drop the technology entirely. We find that establishments that use advanced technologies exhibit higher productivity. This relationship is observed in both 1988 and 1993 even after accounting for other important factors associated with productivity: size, age, capital intensity, labor skill mix, and other controls for plant characteristics such as industry and region. In addition, the relationship between productivity and advanced technology use is observed both in the extent of technologies used and the intensity of their use. Finally, while there is some evidence that the use of advanced technologies is positively related to improved productivity performance, the data suggest that the dominant explanation for the observed cross-section relationship is that good performers are more likely to use advanced technologies than poorly performing operations.
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Technology Locks, Creative Destruction And Non-Convergence In Productivity Levels
April 1995
Working Paper Number:
CES-95-06
This paper presents a simple solution to a new model that seeks to explain the distribution of plants across productivity levels within an industry, and empirically confirms some key predictions using the U.S. textile industry. In the model, plants are locked into a given productivity level, until they exit or retool. Convex costs of adjustment captures the fact that more productive plants expand faster. Provided there is technical change, productivity levels do not converge; the model achieves persistent dispersion in productivity levels within the context of a distortion free competitive equilibrium. The equilibrium, however, is rather turbulent; plants continually come on line with the cutting edge technology, gradually expand and finally exit or retool when they cease to recover their variable costs. The more productive plants create jobs, while the less productive destroy them. The model establishes a close link between productivity growth and dispersion in productivity levels; more rapid productivity growth leads to more widespread dispersion. This prediction is empirically confirmed. Additionally, the model provides an explanation for S-shaped diffusion.
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Whittling Away At Productivity Dispersion
March 1995
Working Paper Number:
CES-95-05
In any time period, in any industry, plant productivity levels differ widely and this dispersion is persistent. This paper explores the sources of this dispersion and their relative magnitudes in the textile industry. Plants that are measured as being more productive but pay higher wages are not necessarily more profitable; wage dispersion can account for approximately 15 percent of productivity dispersion. A plant that is highly productive today may not be as productive tomorrow. I develop a new method for measuring ex-ante dispersion and the percentage of dispersion "explained" by mean reversion. Mean reversion accounts for as much as one half the observed productivity dispersion. A portion of the dispersion, however, appears to reflect real quality differences between plants; plants that are measured as being more productive expand faster and are less likely to exit.
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