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Output 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.
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The Impact of Vintage and Survival on Productivity: Evidence from Cohorts of U.S. Manufacturing Plants
May 2000
Working Paper Number:
CES-00-06
This paper examines the evolution of productivity in U.S. manufacturing plants from 1963 to 1992. We define a 'vintage effect' as the change in productivity of recent cohorts of new plants relative to earlier cohorts of new plants, and a 'survival effect' as the change in productivity of a particular cohort of surviving plants as it ages. The data show that both factors contribute to industry productivity growth, but play offsetting roles in determining a cohort's relative position in the productivity distribution. Recent cohorts enter with significantly higher productivity than earlier entrants did, while surviving cohorts show significant increases in productivity as they age. These two effects roughly offset each other, however, so there is a rough convergence in productivity across cohorts in 1992 and 1987. (JEL Code: D24, L6)
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Industrial Spillovers In Developing Countries: Plant-Level Evidence From Chile, Mexico And Morocco
January 1998
Working Paper Number:
CES-98-02
Recent trade and growth models have underscored the potential importance of external economies of scale. However, many of the most frequently modeled externalities have either not been measured or have been estimated with data too aggregate to be informative. In this paper, plant-level longitudinal data from Chile, Mexico and Morocco allow me to provide some of the first micro evidence on several types of external economies from plant-level production functions. The results indicate that in many industries own-industry output contributes positively to plant-level productivity. However, the effects of geographic concentration are mixed. Cross-country concentration, as measured by a geographic GINI index, often decreases productivity but within-province, same industry activity enhances it.
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Industrial Spillovers in Developing Countries: Plant-level Evidence From Chile, Mexico, and Morocco
January 1998
Working Paper Number:
CES-98-01
This paper documents the procedure used to match firm-level data from the Quarterly Financial Reports (QFR) to plant-level (establishment) data from the Longitudinal Research Database (LRD). The resulting matched firms and their plants provide a link between a firm's financial structure and its manufacturing plants. The linked database provides a resource that researchers can use to examine the interaction of financial structure with firm decisions - including decisions such as employment, investment, mergers, and asset redeployment. Financial structure characteristics in the QFR include the composition and amount of debt claims.
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The Structure of Firm R&D and the Factor Intensity of Production
October 1997
Working Paper Number:
CES-97-15
This paper studies the influence of the structure of firm R&D, industry R&D spillovers, and plant level physical capital on the factor intensity of production. By the structure of firm R&D we mean its distribution across states and products. By factor intensity we mean the cost shares of variable factors, which in this paper are blue collar labor, white collar labor, and materials. We characterize the effect of the structure of firm R&D on factor intensity using a Translog cost function with quasi-fixed factors. This cost function gives rise to a system of variable cost shares that depends on factor prices, firm and industry R&D, and physical capital.
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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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The Missing Link: Technology, Productivity, and Investment
October 1995
Working Paper Number:
CES-95-12
This paper examines the relationship between productivity, investment, and age for over 14,000 plants in the U.S. manufacturing sector in the 1972-1988 period. Productivity patterns vary significantly due to plant heterogeneity. Productivity first increases and then decreases with respect to plant age, and size and industry are systematically correlated with productivity and productivity growth. However, there is virtually no observable relationship between investment and productivity or productivity growth. Overall, the results indicate that plant heterogeneity and fixed effects are more important determinants of observable productivity patterns than sunk costs or capital reallocation. Key Words: productivity, investment, technical change
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