This paper measures plant-level productivity gains associated with learning curves across the entire manufacturing sector. We measure these gains at plant startups and also after major employment changes. We find: 1.) The gains are strongly associated with a variety of human capital measures implying that learning-by-doing is largely a firm-specific human capital investment. 2.) This implicit investment is large; many plants invest as much in learning-by-doing as they invest in physical capital and much more than they invest in formal job training. 3.) This investment differs persistently over industries and is higher with greater R&D. 4.) Consistent with a learning-by-doing interpretation, the human capital investment is much larger following employment decreases than increases. We conclude that learning-by-doing is a major factor in wage determination, technical progress and asymmetric employment adjustment costs.
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Job Reallocation And The Business Cycle: New Facts An Old Debate
September 1998
Working Paper Number:
CES-98-11
This paper provides new facts on the nature of job reallocation over the business cycle, and addresses the question of whether reallocation causes recessions or recessions cause reallocation. Although we do not resolve the question of causality, two general findings emerge that advance our understanding of job reallocation and business cycles. First, much of the cyclical fluctuation in gross job flows occurs in larger plants with relatively moderate employment growth that tends to be transitory, especially at medium-term horizons (up to five years). Unusually large employment growth rates, especially plant startups and shutdowns, are primarily small-plant phenomena and tend to be permanent, less cyclical, and occur later in recessions. Further, high job flow rates occur primarily in plants previously experiencing sharp employment contractions or expansions. Second, key variables that should determine the allocation factors of production across plants and sectors do in fact appear to be related to gross job flows, particularly job destruction. Relative prices, productivity, and investment exhibit time series correlations with job reallocation that suggest that allocative driving forces may contribute significantly to business cycle fluctuations.
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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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The Effects Of Leveraged Buyouts On Productivity And Related Aspects Of Firm Behavior
July 1989
Working Paper Number:
CES-89-05
We investigate the economic effects of leveraged buyouts (LBOs) using large longitudinal establishment and firm-level Census Bureau data sets linked to a list of LBOs compiled from public data sources. About 5 percent, or 1100, of the manufacturing plants in the sample were involved in LBOs during 1981-1986. We find that plants involved in LBOs had significantly higher rates of total-factor productivity (TFP) growth than other plants in the same industry. The productivity impact of LBOs is much larger than our previous estimates of the productivity impact of ownership changes in general. Management buyouts appear to have a particularly strong positive effect on TFP. Labor and capital employed tend to decline (relative to the industry average) after the buyout, but at a slower rate than they did before the buyout. The ratio of nonproduction to production labor cost declines sharply, and production worker wage rates increase, following LBOs. LBOs are production-labor-using, nonproduction-labor-saving, organizational innovations. Plants involved in management buyouts (but not in other LBOs) are less likely to subsequently close than other plants. The average R&D- intensity of firms involved in LBOs increased at least as much from 1978 to 1986 as did the average R&D-intensity of all firms responding to the NSF/Census survey of industrial R&D.
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Managerial Efficiency, Organizational Capital and Productivity
March 2003
Working Paper Number:
CES-03-08
The paper focuses on the impact of managerial efficiency on output. Three sources of managerial efficiency are identified: (a) superior initial managerial endowments, (b) the accumulation of managerial knowledge and skills through learning and (c) the impact of an effective market for managerial resources internal to the firm. All three are explicitly measured by appropriate variables and their impact is examined in the context of variously specified production functions. The empirical analysis is carried out with data for approximately 5,000 new manufacturing plants in the United States over the 1973-92 period. It is found that variation in managerial endowments is an important explanatory variable for output with all other relevant inputs controlled. It is further found that the survival of plants with superior managerial efficiency, and the death of those with inferior efficiency, explains a substantial fraction of total factor productivity change in the manufacturing sector of the U.S. economy. There is also clear evidence of the significance for efficiency of internal markets as well as evidence of learning as plants age. Learning and superior managerial resources of old plants largely offset the benefits of capital goods of later vintage of new plants.
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Learning by Doing and Plant Characteristics
August 1996
Working Paper Number:
CES-96-05
Learning by doing, especially spillover learning, has received much attention lately in models of industry evolution and economic growth. The predictions of these models depend on the distribution of learning abilities and knowledge flows across firms and countries. However, the empirical literature provides little guidance on these issues. In this paper, I use plant level data on a sample of entrants in SIC 38, Instruments, to examine the characteristics associated with both proprietary and spillover learning by doing. The plant level data permit tests for the relative importance of within and between firm spillovers. I include both formal knowledge, obtained through R&D expenditures, and informal knowledge, obtained through learning by doing, in a production function framework. I allow the speed of learning to vary across plants according to characteristics such as R&D intensity, wages, and the skill mix. The results suggest that (a) Ainformal@ knowledge, accumulated through production experience at the plant, is a much more important source of productivity growth for these plants than is Aformal@ knowledge gained via research and development expenditures, (b) interfirm spillovers are stronger than intrafirm spillovers, (c) the slope of the own learning curve is positively related to worker quality, (d) the slope of the spillover learning curve is positively related to the skill mix at plants, (e) neither own nor spillover learning curve slopes are related to R&D intensities. These results imply that learning by doing may be, to some extent, an endogenous phenomenon at these plants. Thus, models of industry evolution that incorporate learning by doing may need to be revised. The results are also broadly consistent with the recent growth models.
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Decomposing Learning By Doing in New Plants
December 1992
Working Paper Number:
CES-92-16
The paper examines learning by doing in the context of a production function in which the other arguments are labor, human capital, physical capital, and vintage as a proxy for embodied technical change in physical capital. Learning is further decomposed into organization learning, capital learning, and manual task learning. The model is tested with time series and cross section data for various samples of up to 2,150 plants over a 14 year period. Word Perfect Version
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The Demand for Human Capital: A Microeconomic Approach
December 2001
Working Paper Number:
CES-01-16
We propose a model for explaining the demand for human capital based on a CES production function with human capital as an explicit argument in the function. The resulting factor demand model is tested with data on roughly 6,000 plants from the Census Bureau's Longitudinal Research Database. The results show strong complementarity between physical and human capital. Moreover, the complementarity is greater in high than in low technology industries. The results also show that physical capital of more recent vintage is associated with a higher demand for human capital. While the age of a plant as a reflection of learning-by-doing is positively related to the accumulation of human capital, this relation is more pronounced in low technology industries.
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The Life Cycles of Industrial Plants
October 2001
Working Paper Number:
CES-01-10
The paper presents a dynamic programming model with multiple classes of capital goods to explain capital expenditures on existing plants over their lives. The empirical specification shows that the path of capital expenditures is explained by (a) complementarities between old and new capital goods, (b) the age of plants, (c) an index that captures the rate of technical change and (d) the labor intensiveness of a plant when it is newly born. The model is tested with Census data for roughly 6,000 manufacturing plants that were born after 1972.
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Declining Migration wihin the US: The Role of the Labor Market
October 2013
Working Paper Number:
CES-13-53
Interstate migration has decreased steadily since the 1980s. We show that this trend is not related to demographic and socioeconomic factors, but that it appears to be connected to a concurrent secular decline in labor market transitions'i.e. the fraction of workers changing employer, industry or occupation. We explore a number of reasons for the dual trends in geographic and labor market transitions, including changes in the distribution of job opportunities across space, polarization in the labor market, concerns of dual-career households, and changes in the net benefit to changing employers. We find little empirical support for all but the last of these hypotheses. Specifically, using data from three cohorts of the National Longitudinal Surveys spanning the 1970s to the 2000s, we find that wage gains associated with employer transitions have fallen, while the returns to staying with the same employer have not changed. We favor the interpretation that, at least from the 1990s to the 2000s, the distribution of outside offers has shifted in a way that has made labor market transitions, and thus geographic transitions, less desirable to workers.
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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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