Papers Containing Keywords(s): 'productivity differences'
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Authors
Sang V Nguyen - 4
Lucia Foster - 3
Viewing papers 1 through 10 of 13
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Working PaperOpening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.View Full Paper PDF
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Working PaperDispersion in Dispersion: Measuring Establishment-Level Differences in Productivity
April 2018
Working Paper Number:
CES-18-25RR
We describe new experimental productivity statistics, Dispersion Statistics on Productivity (DiSP), jointly developed and published by the Bureau of Labor Statistics (BLS) and the Census Bureau. Productivity measures are critical for understanding economic performance. Official BLS productivity statistics, which are available for major sectors and detailed industries, provide information on the sources of aggregate productivity growth. A large body of research shows that within-industry variation in productivity provides important insights into productivity dynamics. This research reveals large and persistent productivity differences across businesses even within narrowly defined industries. These differences vary across industries and over time and are related to productivity-enhancing reallocation. Dispersion in productivity across businesses can provide information about the nature of competition and frictions within sectors, and about the sources of rising wage inequality across businesses. Because there were no official statistics providing this level of detail, BLS and the Census Bureau partnered to create measures of within-industry productivity dispersion. These measures complement official BLS aggregate and industry-level productivity growth statistics and thereby improve our understanding of the rich productivity dynamics in the U.S. economy. The underlying microdata for these measures are available for use by qualified researchers on approved projects in the Federal Statistical Research Data Center (FSRDC) network. These new statistics confirm the presence of large productivity differences and we hope that these new data products will encourage further research into understanding these differences.View Full Paper PDF
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Working PaperWhat Drives Differences in Management?
January 2017
Working Paper Number:
CES-17-32
Partnering with the Census we implement a new survey of 'structured' management practices in 32,000 US manufacturing plants. We find an enormous dispersion of management practices across plants, with 40% of this variation across plants within the same firm. This management variation accounts for about a fifth of the spread of productivity, a similar fraction as that accounted for by R&D and twice as much as explained by IT. We find evidence for four 'drivers' of management: competition, business environment, learning spillovers and human capital. Collectively, these drivers account for about a third of the dispersion of structured management practices.View Full Paper PDF
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Working PaperComputer Networks and Productivity Revisited: Does Plant Size Matter? Evidence and Implications
September 2010
Working Paper Number:
CES-10-25
Numerous studies have documented a positive association between information technology (IT) investments and business- and establishment-level productivity, but these studies usually pay sole or disporportionate attention to small- or medium-sized entities. In this paper, we revisit the evidence for manufacturing plants presented in Atrostic and Nguyen (2005) and show that the positive relationship between computer networks and labor productivity is only found among small- and medium-sized plants. Indeed, for larger plants the relationship is negative, and employment-weighted estimates indicate computer networks have a negative relationship with the productivity of employees, on average. These findings indicate that computer network investments may have an ambiguous relationship with aggregate labor productivity growth.View Full Paper PDF
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Working PaperComputer 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.View Full Paper PDF
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Working PaperHow Businesses Use Information Technology: Insights for Measuring Technology and Productivity
June 2006
Working Paper Number:
CES-06-15
Business use of computers in the United States dates back fifty years. Simply investing in information technology is unlikely to offer a competitive advantage today. Differences in how businesses use that technology should drive differences in economic performance. Our previous research found that one business use ' computers linked into networks ' is associated with significantly higher labor productivity. In this paper, we extend our analysis with new information about the ways that businesses use their networks. Those data show that businesses conduct a variety of general processes over computer networks, such as order taking, inventory monitoring, and logistics tracking, with considerable heterogeneity among businesses. We find corresponding empirical diversity in the relationship between these on-line processes and productivity, supporting the heterogeneity hypothesis. On-line supply chain activities such as order tracking and logistics have positive and statistically significant productivity impacts, but not processes associated with production, sales, or human resources. The productivity impacts differ by plant age, with higher impacts in new plants. This new information about the ways businesses use information technology yields vital raw material for understanding how using information technology improves economic performance.View Full Paper PDF
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Working PaperOutput Market Segmentation and Productivity
June 2001
Working Paper Number:
CES-01-07
Recent empirical investigations have shown enormous plant-level productivity heterogeneity, even within narrowly defined industries. Most of the theoretical explanations for this have focused on factors that influence the production process, such as idiosyncratic technology shocks or input price differences. I claim that characteristics of the output demand markets can also have predictable influences on the plant-level productivity distribution within an industry. Specifically, an industry's degree of output market segmentation (i.e., the substitutability of one plant's output for another's in that industry) should impact the dispersion and central tendency of the industry's plant-level productivity distribution. I test this notion empirically by seeing if measurable cross-sectional variation in market segmentation affects moments of industry's plant-level productivity distribution moments. I find significant and robust evidence consistent with this notion.View Full Paper PDF
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Working PaperAre 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."View Full Paper PDF
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Working PaperIT 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.View Full Paper PDF
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Working PaperARE 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.View Full Paper PDF
