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Computer Network Use and Firms' Productivity Performance: The United States vs. Japan
September 2008
Working Paper Number:
CES-08-30
This paper examines the relationship between computer network use and firms' productivity performance, using micro-data of the United States and Japan. To our knowledge, this is the first comparative analysis using firm-level data for the manufacturing sector of both countries. We find that the links between IT and productivity differ between U.S. and Japanese manufacturing. Computer networks have positive and significant links with labor productivity in both countries. However, that link is roughly twice as large in the U.S. as in Japan. Differences in how businesses use computers have clear links with productivity for U.S. manufacturing, but not in Japan. For the United States, the coefficients of the intensity of network use are positive and increase with the number of processes. Coefficients of specific uses of those networks are positive and significant. None of these coefficients are significant for Japan. Our findings are robust to alternative econometric specifications. They also are robust to expanding our sample from single-unit manufacturing firms, which are comparable in the two data sets, to the entire manufacturing sector in each country, as well as to the wholesale and retail sector of Japan.
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Measuring U.S. Innovative Activity
March 2007
Working Paper Number:
CES-07-11
Innovation has long been credited as a leading source of economic strength and vitality in the United States because it leads to new goods and services and increases productivity, leading to better living standards. Better measures of innovative activities'activities including but not limited to innovation alone'could improve what we know about the sources of productivity and economic growth. The U.S. Census Bureau either currently collects, or has collected, data on some measures of innovative activities, such as the diffusion of innovations and technologies, human and organizational capital, entrepreneurship and other worker and firm characteristics, and the entry and exit of businesses, that research shows affect productivity and other measures of economic performance. But developing an understanding of how those effects work requires more than just measures of innovative activity. It also requires solid statistical information about core measures of the economy: that is, comprehensive coverage of all industries, including improved measures of output and sales and additional information on inputs and purchased materials at the micro (enterprise) level for the same economic unit over time (so the effects can be measured). Filling gaps in core data would allow us to rule out the possibility that a measure of innovative activity merely proxies for something that is omitted from or measured poorly in the core data, provide more information about innovative activities, and strengthen our ability to evaluate the performance of the entire economy. These gaps can be filled by better integrating existing data and by more structured collections of new data.
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The Importance of Reallocations in Cyclical Productivity and Returns to Scale: Evidence from Plant-Level Data
March 2007
Working Paper Number:
CES-07-05
This paper provides new evidence that estimates based on aggregate data will understate the true procyclicality of total factor productivity. I examine plant-level data and show that some industries experience countercyclical reallocations of output shares among firms at different points in the business cycle, so that during recessions, less productive firms produce less of the total output, but during expansions they produce more. These reallocations cause overall productivity to rise during recessions, and do not reflect the actual path of productivity of a representative firm over the course of the business cycle. Such an effect (sometimes called the cleansing effect of recessions) may also bias aggregate estimates of returns to scale and help explain why decreasing returns to scale are found at the industry-level data.
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Computer Investment, Computer Networks and Productivity
January 2005
Working Paper Number:
CES-05-01
Researchers in a large empirical literature find significant relationships between computers and labor productivity, but the estimated size of that relationship varies considerably. In this paper, we estimate the relationships among computers, computer networks, and plant-level productivity in U.S. manufacturing. Using new data on computer investment, we develop a sample with the best proxies for computer and total capital that the data allow us to construct. We find that computer networks and computer inputs have separate, positive, and significant relationships with U.S. manufacturing plant-level productivity. Keywords: computer input; information technology; labor productivity
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The Relation among Human Capital, Productivity and Market Value: Building Up from Micro Evidence
December 2002
Working Paper Number:
tp-2002-14
This paper investigates and evaluates the direct and indirect contribution of human capital
to business productivity and shareholder value. The impact of human capital may occur in two ways:
the specific knowledge of workers at businesses may directly increase business
performance, or a skilled workforce may also indirectly act as a complement to improved
technologies, business models or organizational practices. We use newly created firm-level
measures of workforce human capital and productivity to examine links between those measures
and the market value of the employing firm. The new human capital measures come from an
integrated employer-employee data base under development at the US Census Bureau. We link
these data to financial information from Compustat at the firm level, which provides measures of
market value and tangible assets. The combination of these two sources permits examination of
the link between human capital, productivity, and market value. There is a substantial positive
relation between human capital and market value that is primarily related to the unmeasured
personal characteristics of the employees, which are captured by the new measures.
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U.S. Productivity and Electronic Processes in Manufacturing
October 2001
Working Paper Number:
CES-01-11
Recent studies argue that the use of information technology is a significant source of U.S. productivity growth. Official U.S. data on this use have been scarce. New official data on the use of electronic business processes (business processes such as procurement, payroll, inventory, etc.,conducted over computer networks) in the manufacturing sector of the United States were recently released. Preliminary estimates based on these data are consistent with some results in the literature. However, they also raise questions requiring additional detailed micro data analysis.
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Plant Vintage, Technology, and Environmental Regulation
September 2001
Working Paper Number:
CES-01-08
Does the impact of environmental regulation differ by plant vintage and technology? We answer this question using annual Census Bureau information on 116 pulp and paper mills' vintage, technology, productivity, and pollution abatement operating costs for 1979-1990. We find a significant negative relationship between pollution abatement costs and productivity levels. This is due almost entirely to integrated mills (those incorporating a pulping process), where a one standard deviation increase in abatement costs is predicted to reduce productivity by 5.4 percent. Older plants appear to have lower productivity but are less sensitive to abatement costs, perhaps due to 'grandfathering' of regulations. Mills which undergo renovations are also less sensitive to abatement costs, although these vintage and renovation results are not generally significant. We find similar results using a log-linear version of a three input Cobb-Douglas production function in which we include our technology, vintage, and renovation variables. Sample calculations of the impact of pollution abatement on productivity show the importance of allowing for differences based on plant technology. In a model incorporating technology interactions we estimate that total pollution abatement costs reduce productivity levels by an average of 4.7 percent across all the plants. The comparable estimate without technology interactions is 3.3 percent, approximately 30% lower.
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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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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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