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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Spinout Formation: Do Opportunities and Constraints Benefit High Capital Founders?
June 2015
Working Paper Number:
CES-15-07
We examine the role of human capital in employees' decisions to leave their parent firms andform spinouts. Using a large sample of individuals who formed spinouts in manufacturing industries between 1992 and 2005, and their co-workers who did not, we find that after controlling for age, education level, gender and alien status, individuals with higher human capital (measured as their earnings or experience) are more likely to form spinouts. We then examine the impact of industry opportunities and constraints on the propensity of high human capital individuals to form spinouts. Counterintuitively, we find that both industry constraints (measured as industry capital intensity) and opportunities (industry R&D intensity) reduce the propensity of higher human capital individuals to form spinouts. We interpret these results as being consistent with the argument that high human capital founders are more likely to choose larger, more capital-intensive projects than low human capital individuals, and thus face greater constraints. On the other side, R&D intensive industries appear to present abundant entrepreneurial opportunities, allowing low human capital individuals to identify their own opportunities thus decreasing the relative advantage of high human capital individuals.
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The Survival of Industrial Plants
October 2002
Working Paper Number:
CES-02-25
The study seeks to explain the attrition rate of new manufacturing plants in the United States in terms of three vectors of variables. The first explains how survival of the fittest proceeds through learning by firms (plants) about their own relative efficiency. The second explains how efficiency systematically changes over time and what augments or diminishes it. The third captures the opportunity cost of resources employed in a plant. The model is tested using maximum-likelihood probit analysis with very large samples for successive census years in the 1967-97 period. One sample consists of an unbalanced panel of about three-fourths of a million plants of single and multi-unit firms, or alternatively of about 300,000 plants if only the most reliable data are considered. The second is restricted to the plants of multi-unit firms in the same time span and consists of an unbalanced panel of more than 100,000 plants. The empirical analysis strongly confirms the predictions of the model.
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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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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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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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Incidence and Performance of Spinouts and Incumbent New Ventures: Role of Selection and
Redeployability within Parent Firms
September 2021
Working Paper Number:
CES-21-27
Using matched employer-employee data from 30 U.S. states, we compare spinouts with new ventures formed by incumbents (INCs). We propose a selection-based framework comprising idea selection by parents to internally implement ideas as INCs, entrepreneurial selection by founders to form spinouts, and managerial selection to close ventures. Consistent with parents choosing better ideas in the idea selection stage, we find that INCs perform relatively better than spinouts, and more so with larger parents. Regarding the entrepreneurial selection stage, we find evidence consistent with resource requirements being a greater entry barrier to spinouts and greater information asymmetry promoting spinout formation. Parents' resource redeployment opportunities are associated with lower relative survival of INCs, consistent with their being subject to greater selection pressures in the managerial selection stage.
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The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing
June 2020
Working Paper Number:
CES-20-18
This paper estimates stochastic frontier energy demand functions with non-public, plant-level data from the U.S. Census Bureau to measure the energy efficiency gap and energy price elasticities in the food processing industry. The estimates are for electricity and fuel use in 4 food processing sectors, based on the disaggregation of this industry used by the National Energy Modeling System Industrial Demand Module. The estimated demand functions control for plant inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. Own price elasticities range from 0.6 to -0.9 with little evidence of fuel/electricity substitution. The magnitude of the efficiency estimates is sensitive to the assumptions but consistently reveal that few plants achieve 100% efficiency. Defining a 'practical level of energy efficiency' as the 95th percentile of the efficiency distributions and averaging across all the models result in a ~20% efficiency gap. However, most of the potential reductions in energy use from closing this efficiency gap are from plants that are 'low hanging fruit'; 13% of the 20% potential reduction in the efficiency gap can be obtained by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industry; ranging from a low of 0.4% to a high of 5.7%.
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Learning By Doing And Competition In The Early Rayon Industry
February 1993
Working Paper Number:
CES-93-04
In this paper, I derive a structural econometric model of learning by doing from a dynamic oligopoly game. Unlike previous empirical models, this model is capable of testing hypotheses concerning both the technological nature and behavioral implications of learning. I estimate the model with firm level data from the early U.S. rayon industry. The empirical results show that there were considerable differences across firms in both proprietary and spillover learning. The results also indicate that two of the three firms took their rival's reactions into account when choosing their strategies.
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Asymmetric Learning Spillovers
April 1993
Working Paper Number:
CES-93-07
In this paper, I employ a linear-quadratic model of an industry characterized by learning by doing to examine the implications of asymmetric learning spillovers. Importantly, I show that distribution of spillover benefits can influence market structure in ways that can not be seen in models where spillovers are symmetric. If spillovers are asymmetric, a tradeoff between improved industry performance and increased market concentration can arise which does not occur when they are symmetric. This tradeoff leads to a policy dilemma; whether to promote static or dynamic efficiency in markets where learning is important.
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Plant-Level Productivity and the Market Value of a Firm
June 2001
Working Paper Number:
CES-01-03
Some plants are more productive than others ' at least in terms of how productivity is conventionally measured. Do these differences represent an intangible asset? Does the stock market place a higher value on firms with highly productive plants? This paper tests this hypothesis with a new data set. We merge plant-level fundamental variables with firm-level financial variables. We find that firms with highly productive plants have higher market valuations as measured by Tobin's q ' productivity does indeed have a price.
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