Papers Containing Keywords(s): 'productivity analysis'
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Authors
Lucia Foster - 3
Sang V Nguyen - 3
Viewing papers 1 through 10 of 12
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Working PaperDecomposing Aggregate Productivity
July 2022
Working Paper Number:
CES-22-25
In this note, we evaluate the sensitivity of commonly-used decompositions for aggregate productivity. Our analysis spans the universe of U.S. manufacturers from 1977 to 2012 and we find that, even holding the data and form of the production function fixed, results on aggregate productivity are extremely sensitive to how productivity at the firm level is measured. Even qualitative statements about the levels of aggregate productivity and the sign of the covariance between productivity and size are highly dependent on how production function parameters are estimated. Despite these difficulties, we uncover some consistent facts about productivity growth: (1) labor productivity is consistently higher and less error-prone than measures of multi-factor productivity; (2) most productivity growth comes from growth within firms, rather than from reallocation across firms; (3) what growth does come from reallocation appears to be driven by net entry, primarily from the exit of relatively less-productive firms.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 PaperManagement in America
January 2013
Working Paper Number:
CES-13-01
The Census Bureau recently conducted a survey of management practices in over 30,000 plants across the US, the first large-scale survey of management in America. Analyzing these data reveals several striking results. First, more structured management practices are tightly linked to better performance: establishments adopting more structured practices for performance monitoring, target setting and incentives enjoy greater productivity and profitability, higher rates of innovation and faster employment growth. Second, there is a substantial dispersion of management practices across the establishments. We find that 18% of establishments have adopted at least 75% of these more structured management practices, while 27% of establishments adopted less than 50% of these. Third, more structured management practices are more likely to be found in establishments that export, who are larger (or are part of bigger firms), and have more educated employees. Establishments in the South and Midwest have more structured practices on average than those in the Northeast and West. Finally, we find adoption of structured management practices has increased between 2005 and 2010 for surviving establishments, particularly for those practices involving data collection and analysis.View Full Paper PDF
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Working PaperPlant-Level Responses to Antidumping Duties: Evidence from U.S. Manufacturers
October 2009
Working Paper Number:
CES-09-38R
This paper describes the effects of a temporary increase in tariffs on the performance and behavior of U.S. manufacturers. Using antidumping duties as an example of temporary protection, I compare the responses of protected manufacturers to those predicted by models of trade with heterogeneous firms. I find that apparent increases in revenue productivity associated with antidumping duties are primarily due to increases in prices and mark-ups, as physical productivity falls among protected plants. Moreover, antidumping duties slow the reallocation of resources from less productive to more productive uses by reducing product-switching behavior among protected plants.View Full Paper PDF
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Working PaperReallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?
September 2005
Working Paper Number:
CES-05-11
There is considerable evidence that producer-level churning contributes substantially to aggregate (industry) productivity growth, as more productive businesses displace less productive ones. However, this research has been limited by the fact that producer-level prices are typically unobserved; thus within-industry price differences are embodied in productivity measures. If prices reflect idiosyncratic demand or market power shifts, high 'productivity' businesses may not be particularly efficient, and the literature's findings might be better interpreted as evidence of entering businesses displacing less profitable, but not necessarily less productive, exiting businesses. In this paper, we investigate the nature of selection and productivity growth using data from industries where we observe producer-level quantities and prices separately. We show there are important differences between revenue and physical productivity. A key dissimilarity is that physical productivity is inversely correlated with plant-level prices while revenue productivity is positively correlated with prices. This implies that previous work linking (revenue-based) productivity to survival has confounded the separate and opposing effects of technical efficiency and demand on survival, understating the true impacts of both. We further show that young producers charge lower prices than incumbents, and as such the literature understates the productivity advantage of new producers and the contribution of entry to aggregate productivity growth.View Full Paper PDF
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Working PaperComputer 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 productivityView Full Paper PDF
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Working PaperProductivity Growth Patterns in U.S. Food Manufacturing: Case of Dairy Products Industry
May 2004
Working Paper Number:
CES-04-08
A panel constructed from the Census Bureau's Longitudinal Research Database is used to measure total factor productivity growth at the plant-level and analyzes the multifactor bias of technical change at three-digit product group level containing five different four-digit sub-group categories for the U.S. dairy products industry from 1972 through 1995. In the TFP growth decomposition, analyzing the growth and its components according to the quartile ranks show that scale effect is the most significant element of TFP growth except the plants in the third quartile rank where technical change dominates throughout the time periods. The exogenous input bias results show that throughout the time periods, technical change is 1) capital-using; 2) labor-using after 1980; 3) material-saving except 1981-1985 period; and, 4) energy-using except 1981-1985 and 1991-1995 periods. Plant productivity analysis indicate that less than 50% of the plants in the dairy products industry stay in the same category, indicating considerable movement between productivity rank categories. Investment analysis results indicate that plant-level investments are quite lumpy since a relatively small percent of observations account for a disproportionate share of overall investment. Productivity growth is found to be positively correlated with recent investment spikes for plants with TFP ranking in the middle two quartiles and uncorrelated with plants in the smallest and largest quartiles. Similarly, past TFP growth rates present no significant correlation with future investment spikes for plants in any quartile.View Full Paper PDF
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Working PaperProductivity Growth Patterns in U.S. Food Manufacturing: Case of Meat Products Industry
March 2004
Working Paper Number:
CES-04-04
A panel constructed from the Census Bureau's Longitudinal Research Database is used to measure total factor productivity growth at the plant-level and analyzes the multifactor bias of technical change for the U.S. meat products industry from 1972 through 1995. For example, addressing TFP growth decomposition for the meat products sub-sector by quartile ranks shows that the technical change effect is the dominant element of TFP growth for the first two quartiles, while the scale effect dominates TFP growth for the higher two quartiles. Throughout the time period, technical change is 1) capital-using; 2) material-saving; 3) labor-using; and, 4) energy-saving and becoming energy-using after 1980. The smaller sized plants are more likely to fluctuate in their productivity rankings; in contrast, large plants are more stable in their productivity rankings. Plant productivity analysis indicate that less than 50% of the plants in the meat industry stay in the same category, indicating considerable movement between productivity rank categories. Investment analysis results strongly indicate that plant-level investments are quite lumpy since a relatively small percent of observations account for a disproportionate share of overall investment. Productivity growth is found to be positively correlated with recent investment spikes for plants with TFP ranking in the middle two quartiles and uncorrelated with firms in the smallest and largest quartiles. Similarly, past TFP growth rates are positively correlated with future investment spikes for firms in the same quartiles. \View Full Paper PDF
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Working PaperPollution Abatement Expenditures and Plant-Level Productivity: A Production Function Approach
August 2003
Working Paper Number:
CES-03-16
In this paper, we investigate the impact of environmental regulation on productivity using a Cobb-Douglas production function framework. Estimating the effects of regulation on productivity can be done with a top-down approach using data for broad sectors of the economy, or a more disaggregated bottom-up approach. Our study follows a bottom-up approach using data from the U.S. paper, steel, and oil industries. We measure environmental regulation using plant-level information on pollution abatement expenditures, which allows us to distinguish between productive and abatement expenditures on each input. We use annual Census Bureau information (1979-1990) on output, labor, capital, and material inputs, and pollution abatement operating costs and capital expenditures for 68 pulp and paper mills, 55 oil refineries, and 27 steel mills. We find that pollution abatement inputs generally contribute little or nothing to output, especially when compared to their 'productive' equivalents. Adding an aggregate pollution abatement cost measure to a Cobb-Douglas production function, we find that a $1 increase in pollution abatement costs leads to an estimated productivity decline of $3.11, $1.80, and $5.98 in the paper, oil, and steel industries respectively. These findings imply substantial differences across industries in their sensitivity to pollution abatement costs, arguing for a bottom-up approach that can capture these differences. Further differentiating plants by their production technology, we find substantial differences in the impact of pollution abatement costs even within industries, with higher marginal costs at plants with more polluting technologies. Finally, in all three industries, plants concentrating on change-in-production-process abatement techniques have higher productivity than plants doing predominantly end-of-line abatement, but also seem to be more affected by pollution abatement operating costs. Overall, our results point to the importance using detailed, disaggregated analyses, even below the industry level, when trying to model the costs of forcing plants to reduce their emissions.View Full Paper PDF
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Working PaperComputer Networks and U.S. Manufacturing Plant Productivity: New Evidence from the CNUS Data
January 2002
Working Paper Number:
CES-02-01
How do computers affect productivity? Many recent studies argue that using information technology, particularly computers, is a significant source of U.S. productivity growth. The specific mechanism remains elusive. Detailed data on the use of computers and computer networks have been scarce. Plant-level data on the use of computer networks and electronic business processes in the manufacturing sector of the United States were collected for the first time in 1999. Using these data, we find strong links between labor productivity and the presence of computer networks. We find that average labor productivity is higher in plants with networks. Computer networks have a positive and significant effect on plant labor productivity after controlling for multiple factors of production and plant characteristics. Networks increase estimated labor productivity by roughly 5 percent, depending on model specification. Model specifications that account for endogenous computer networks also show a positive and significant relationship. Our work differs from others in several important aspects. First, ours is the first study that directly links the use of computer networks to labor productivity using plant-level data for the entire U.S. manufacturing sector. Second, we extend the existing model relating computers to productivity by including materials as an explicit factor input. Third, we test for possible endogeneity problems associated with the computer network variable.View Full Paper PDF
