-
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.
View Full
Paper PDF
-
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.
View Full
Paper PDF
-
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
View Full
Paper PDF
-
Technology Locks, Creative Destruction And Non-Convergence In Productivity Levels
April 1995
Working Paper Number:
CES-95-06
This paper presents a simple solution to a new model that seeks to explain the distribution of plants across productivity levels within an industry, and empirically confirms some key predictions using the U.S. textile industry. In the model, plants are locked into a given productivity level, until they exit or retool. Convex costs of adjustment captures the fact that more productive plants expand faster. Provided there is technical change, productivity levels do not converge; the model achieves persistent dispersion in productivity levels within the context of a distortion free competitive equilibrium. The equilibrium, however, is rather turbulent; plants continually come on line with the cutting edge technology, gradually expand and finally exit or retool when they cease to recover their variable costs. The more productive plants create jobs, while the less productive destroy them. The model establishes a close link between productivity growth and dispersion in productivity levels; more rapid productivity growth leads to more widespread dispersion. This prediction is empirically confirmed. Additionally, the model provides an explanation for S-shaped diffusion.
View Full
Paper PDF
-
Recent Twists of the Wage Structure and Technology Diffusion
March 1994
Working Paper Number:
CES-94-05
This paper is an empirical study of the impact on U.S. wage structure of domestic technology, foreign technology, and import penetration. A model is presented which combines factor proportions theory with a version of growth theory. The model, which assumes two levels of skill, suggests that domestic technology raises both wages, while foreign technology, on a simple interpretation, lowers both. Trade at a constant technology, as usual, lowers the wage of that class of labor used intensively by the affected industry, and raises the other wage. The findings support the predictions of the model for domestic technology. On the other hand, they suggest that technological change, and perhaps other factors, have obscured the role of factor proportions in the data. Indeed, foreign technology and trade have the same effect on wages at different skill levels, not the opposite effects suggested by factor proportions. Finally, a simple diffusion story, in which foreign technology lowers all U.S. wages, is also rejected. Instead, uniformly higher U.S. wages, not lower, appear to be associated with the technology and trade of the oldest trading partners of the U.S., the economies of the West. Not so for Asia, especially the smaller countries which have recently accelerated their trade with the U.S. Their effects are uniformly negative on wages, suggesting a distinction between shock and long run effects of foreign technology and trade.
View Full
Paper PDF
-
Academic Science, Industrial R&D, and the Growth of Inputs
January 1993
Working Paper Number:
CES-93-01
This paper is a theoretical and empirical investigation of the connection between science, R&D, and the growth of capital. Studies of high technology industries and recent labor studies agree in assigning a large role to science and technology in the growth of human and physical capital, although direct tests of these relationships have not been carried out. This paper builds on the search approach to R&D of Evenson and Kislev (1976) to unravel the complex interactions between science, R&D, and factor markets suggested by these studies. In our theory lagged science increases the returns to R&D, so that scientific advance later feeds into growth of R&D. In turn, product quality improvements and price declines lead to the growth of industry by shifting out new product demand, perhaps at the expense of traditional industries. All this tends to be in favor of the human and physical capital used intensively by high technology industries. This is the source of the factor bias which is implicit in the growth of capital per head. Our empirical work overwhelmingly supports the contention that growth of labor skills and physical capital are linked to science and R&D. It also supports the strong sequencing of events that is a crucial feature of our model, first from science to R&D, and later to output and factor markets.
View Full
Paper PDF
-
Wages, Employer Size-Wage Premia and Employment Structure: Their Relationship to Advanced-Technology Usage at U.S. Manufacturing Establishments
December 1992
Working Paper Number:
CES-92-15
We study wages, size-wage premia and the employment structure (measured as the fraction of production workers in an establishment) and their relationship to the extent of advanced-technology usage at U.S, manufacturing plants. We begin by sketching a model of technology adoption based on Lucas (1978) that provides a framework for interpreting the data analysis. We then study a new Census Bureau survey of technology use at manufacturing plants. Workers in establishments that are classified as the most technology intensive earn a premium of 16 percent as compared to those in plants that are the least premium earned by workers in all but the very largest plants. The inclusion of the technology classification variables in standard wage regressions reduced the size-wage premia by as much as 60 percent for some size categories.
View Full
Paper PDF
-
Technology Usage in U.S. Manufacturing Industries: New Evidence from the Survey of Manufacturing Technology
October 1991
Working Paper Number:
CES-91-07
Using a new dataset on technology usage in U.S. manufacturing plants, this paper describes how technology usage varies by plant and firm characteristics. The paper extends the previous literature in three important ways. First, it examines a wide range of relatively new technologies. Second, the paper uses a much larger and more representative set of firms and establishments than previous studies. Finally, the paper explores the role of firm R&D expenditures in the process of technology adoption. The main findings indicate that larger plants more readily use new technologies, plants owned by firms with high R&D-to-sales ratios adopt technologies more rapidly, and the relationship between plant age and technology usage is relatively weak.
View Full
Paper PDF
-
Decomposing Technical Change
May 1991
Working Paper Number:
CES-91-04
A production function is specified with human capital as a separate argument and with embodied technical change proxied by a variable that measures the average vintage of the stock of capital. The coefficients of this production function are estimated with cross section data for roughly 2,150 new manufacturing plants in 41 industries, and for subsets of this sample. The question of interactions between new investment and initial endowments of capital is then examined with data for roughly 1,400 old plants in 15 industries.
View Full
Paper PDF
-
Measuring Total Factor Productivity, Technical Change And The Rate Of Returns To Research And Development
May 1991
Working Paper Number:
CES-91-03
Recent research indicates that estimates of the effect of research and development (R&D) on total factor productivity growth are sensitive to different measures of total factor productivity. In this paper, we use establishment level data for the flat glass industry extracted from the Census Bureau's Longitudinal Research Database (LRD) to construct three competing measures of total factor productivity. We then use these measures to estimate the conventional R&D intensity model. Our empirical results support previous finding that the estimated coefficients of the model are sensitive to the measurement of total factor productivity. Also, when using microdata and more detailed modeling, R&D is found to be a significant factor influencing productivity growth. Finally, for the flat glass industry, a specific technical change index capturing the learning-by-doing process appears to be superior to the conventional time trend index.
View Full
Paper PDF
