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Productivity Adjustments and Learning-by-Doing as Human Capital

November 1997

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The 10 most similar working papers to the working paper 'Productivity Adjustments and Learning-by-Doing as Human Capital' are listed below in order of similarity.

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  Authors: Donald Siegel, Frank R Lichtenberg

  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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• Working Paper

  
  
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  September 1998

  Authors: Scott Schuh, Robert K. Triest

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  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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• Working Paper

  
  
  Decomposing Learning By Doing in New Plants
  

  December 1992

  Authors: Byong-Hyong Bahk, Michael Gort

  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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• Working Paper

  
  
  The Effect Of Takeovers On The Employment And Wages Of Central-Office And Other Personnel
  

  June 1989

  Authors: Donald Siegel, Frank R Lichtenberg

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• Working Paper

  
  
  Industry Learning Environments and the Heterogeneity of Firm Performance
  

  December 2006

  Authors: Natarajan Balasubramanian, Marvin Lieberman

  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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• Working Paper

  
  
  Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity
  

  April 2018

  Authors: John Haltiwanger, Lucia Foster, Cheryl Grim, Zoltan Wolf, Cindy Cunningham, Sabrina Wulff Pabilonia, Jay Stewart

  Working Paper Number:

  CES-18-25RR

  We describe new experimental productivity statistics, Dispersion Statistics on Productivity (DiSP), jointly developed and published by the Bureau of Labor Statistics (BLS) and the Census Bureau. Productivity measures are critical for understanding economic performance. Official BLS productivity statistics, which are available for major sectors and detailed industries, provide information on the sources of aggregate productivity growth. A large body of research shows that within-industry variation in productivity provides important insights into productivity dynamics. This research reveals large and persistent productivity differences across businesses even within narrowly defined industries. These differences vary across industries and over time and are related to productivity-enhancing reallocation. Dispersion in productivity across businesses can provide information about the nature of competition and frictions within sectors, and about the sources of rising wage inequality across businesses. Because there were no official statistics providing this level of detail, BLS and the Census Bureau partnered to create measures of within-industry productivity dispersion. These measures complement official BLS aggregate and industry-level productivity growth statistics and thereby improve our understanding of the rich productivity dynamics in the U.S. economy. The underlying microdata for these measures are available for use by qualified researchers on approved projects in the Federal Statistical Research Data Center (FSRDC) network. These new statistics confirm the presence of large productivity differences and we hope that these new data products will encourage further research into understanding these differences.
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• Working Paper

  
  
  The Life Cycles of Industrial Plants
  

  October 2001

  Authors: Michael Gort, Seong-Hoon Lee

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  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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• Working Paper

  
  
  The Demand for Human Capital: A Microeconomic Approach
  

  December 2001

  Authors: Michael Gort, Seong-Hoon Lee

  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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• Working Paper

  
  
  Labor Market Rigidities and the Employment Behavior of Older Workers
  

  July 2007

  Authors: Tetyana Shvydko, David Blau

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  CES-07-21

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• Working Paper

  
  
  Learning by Doing and Plant Characteristics
  

  August 1996

  Authors: Ron Jarmin

  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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