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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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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Environmental Regulation, Abatement, and Productivity: A Frontier Analysis
September 2013
Working Paper Number:
CES-13-51
This research studies the link between environmental regulation and plant level productivity in two U.S. manufacturing industries: pulp and paper mills and oil refineries using Data Envelopment Analysis (DEA) models. Data on abatement spending, emissions and abated emissions are used in different DEA models to study plant productivity outcomes when accounting for abatement spending or emissions regulations. Results indicate that pulp and paper mills and oil refineries in the U.S. suffered decreases in productivity due to pollution abatement activities from 1974 to 2000. These losses in productivity are substantial but have been slowly trending downwards even when the regulations have tended to become more stringent and emission of pollutants has declined suggesting that the best practice has shifted over time. Results also show that the reported abatement expenditures are not able to explain all the losses arising out of regulation suggesting that these abatement expenditures are consistently under-reported.
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Pollution 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.
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Evaluating the Impact of MEP Services on Establishment Performance: A Preliminary Empirical Investigation
July 2012
Working Paper Number:
CES-12-15
This work examines the impact of manufacturing extension services on establishment productivity. It builds on an earlier study conducted by Jarmin in the 1990s, by matching the Census of Manufacturers (CMF) with the Manufacturing Extension Partnership (MEP) customer and activity datasets to generate treatment and comparison groups for analysis. The scope of the study is the period 1997 to 2002, which was a period of economic downturn in the manufacturing sector and budgetary challenges for the MEP. The paper presents some preliminary findings from this analysis. Both lagged dependent variable (LDV) and difference in difference (DiD) models are employed to estimate the relationship between manufacturing extension and labor productivity. The results presented are inconclusive and paint a mixed picture as they demonstrate the benefits and limitations of using Census microdata in program evaluation. They also point to the need to conduct analyses that could help to better understand the dynamic impact of MEP services.
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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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Computer 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.
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Industry Learning Environments and the Heterogeneity of Firm Performance
December 2006
Working Paper Number:
CES-06-29
This paper characterizes inter-industry heterogeneity in rates of learning-by-doing and examines how industry learning rates are connected with firm performance. Using data from the Census Bureau and Compustat, we measure the industry learning rate as the coefficient on cumulative output in a production function. We find that learning rates vary considerably among industries and are higher in industries with greater R&D, advertising, and capital intensity. More importantly, we find that higher rates of learning are associated with wider dispersion of Tobin's q and profitability among firms in the industry. Together, these findings suggest that learning intensity represents an important characteristic of the industry environment.
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Agglomeration, Enterprise Size, and Productivity
August 2004
Working Paper Number:
CES-04-15
Much research on agglomeration economies, and particularly recent work that builds on Marshall's concept of the industrial district, postulates that benefits derived from proximity between businesses are strongest for small enterprises (Humphrey 1995, Sweeney and Feser 1998). With internal economies a function of the shape of the average cost curve and level of production, and external economies in shifts of that curve, a small firm enjoying external economies characteristic of industrial districts (or complexes or simply urbanized areas) may face the same average costs as the larger firm producing a higher volume of output (Oughton and Whittam 1997; Carlsson 1996; Humphrey 1995). Thus we observe the seeming paradox of large firms that enjoy internal economies of scale co-existing with smaller enterprises that should, by all accounts, be operating below minimum efficient scale. With the Birch-inspired debate on the relative job- and innovation-generating capacity of small and large firms abating (Ettlinger 1997), research on the small firm sector has shifted to an examination of the business strategies and sources of competitiveness of small enterprises (e.g., Pratten 1991, Nooteboom 1993). Technological external scale economies are a key feature of this research (Oughton and Whittam 1997).
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What Determines Environmental Performance at Paper Mills? The Roles of Abatement Spending, Regulation, and Efficiency
April 2003
Working Paper Number:
CES-03-10
This paper examines the determinants of environmental performance at paper mills, measured by air pollution emissions per unit of output. We consider differences across plants in air pollution abatement expenditures, local regulatory stringency, and productive efficiency. Emissions are significantly lower in plants with a larger air pollution abatement capital stock: a 10 percent increase in abatement capital stock appears to reduce emissions by 6.9 percent. This translates into a sizable social return: one dollar of abatement capital stock is estimated to provide and annual return of about 75 cents in pollution reduction benefits. Local regulatory stringency and productive efficiency also matter: plants in non-attainment counties have 43 percent lower emissions and plants with 10 percent higher productivity have 2.5 percent lower emissions. For pollution abatement operating costs we find (puzzlingly) positive, but always insignificant, coefficients.
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The Influence Of Location On Productivity: Manufacturing Technology In Rural And Urban Areas
December 1991
Working Paper Number:
CES-91-10
Policies to counter the growing discrepancy between economic opportunities in rural and urban areas have focused predominantly on expanding manufacturing in rural areas. Fundamental to the design of these strategies are the relative costs of production and productivity of manufacturing in rural and urban areas. This study aims to develop information that can be used to assess the productivity of manufacturing in rural and urban areas. Production functions are estimated in the meat products and household furniture industries to investigate selected aspects of the effect of rural, small urban, and metropolitan location on productivity. The results show that the effect of location on productivity varies with industry, size, and the timing of the entry of the establishment into the industry. While the analysis is specific to two industries, it suggests that development policies targeting manufacturing can be made more effective by focusing on industries and plants with characteristics that predispose them to the locations being supported.
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