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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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Market Structure and Productivity: A Concrete Example
June 2001
Working Paper Number:
CES-01-06
This paper shows that imperfect output substitutability explains part of the observed persistent plant-level productivity dispersion. Specifically, as substitutability in a market increases, the market's productivity distribution exhibits falling dispersion and higher central tendency. The proposed mechanism behind this result is truncation of the distribution from below as increased substitutability shifts demand to lower-cost plants and drives inefficient plants out of business. In a case study of the ready-mixed concrete industry, I examine the impact of one manifestation of this effect, driven by geographic market segmentation resulting from transport costs. A theoretical foundation is presented characterizing how differences in the density of local demand impact the number of producers and the ability of customers to choose between suppliers, and through this, the equilibrium productivity and output levels across regions. I also introduce a new method of obtaining plant-level productivity estimates that is well suited to this application and avoids potential shortfalls of commonly used procedures. I use these estimates to empirically test the presented theory, and the results support the predictions of the model. Local demand density has a significant influence on the shape of plant-level productivity distributions, and accounts for part of the observed intra-industry variation in productivity, both between and within given market areas.
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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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IT Spending and Firm Productivity: Additional Evidence from the Manufacturing Sector
October 1999
Working Paper Number:
CES-99-10
The information systems (IS) "productivity paradox" is based on those studies that found little or no positive relationship between firm productivity and spending on IS. However, some earlier studies and one more recent study have found a positive relationship. Given the large amounts spent by organizations on information systems, it is important to understand the relationship between spending on IS and productivity. Beyond replicating positive results, an explanation is needed for the conflicting conclusions reached by these earlier studies. Data collected by the Bureau of the Census is analyzed to investigate the relationship between plant-level productivity and spending on IS. The relationship between productivity and spending on IS is investigated using assumptions and models similar to both studies with positive findings and studies with negative findings. First, the overall relationship is investigated across all manufacturing industries. Next, the relationship is investigated industry by industry. The analysis finds a positive relationship between plant-level productivity and spending on IS. The relationship is also shown to vary across industries. The conflicting results from earlier studies are explained by understanding the characteristics of the data analyzed in each study. A large enough sample size is needed to find the relatively smaller effect from IS spending as compared to other input spending included in the models. Because the relationship between productivity and IS spending varies across industries, industry mix is shown to be an important data characteristic that may have influenced prior results.
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Aggregate Productivity Growth: Lessons From Microeconomic Evidence
September 1998
Working Paper Number:
CES-98-12
In this study we focus on the role of the reallocation of activity across individual producers for aggregate productivity growth. A growing body of empirical analysis yields striking patterns in the behavior of establishment-level reallocation and productivity. Nevertheless, a review of existing studies yields a wide range of findings regarding the contribution of reallocation to aggregate productivity growth. Through our review of existing studies and our own sensitivity analysis, we find that reallocation plays a significant role in the changes in productivity growth at the industry level and that the impact of net entry is disproportionate since entering plants tend to displace less productive exiting plants, even after controlling for overall average growth in productivity. However, an important conclusion of our sensitivity analysis is that the quantitative contribution of reallocation to the aggregate change in productivity is sensitive to the decomposition methodology employed. Our findings also confirm and extend others in the literature that indicate that both learning and selection effects are important in this context. A novel aspect of our analysis is that we have examined the role of reallocation for aggregate productivity growth to a selected set of service sector industries. Our analysis considers the 4-digit industries that form the 3-digit industry automobile repair shops. We found tremendous churning in this industry with extremely large rates of entry and exit. Moreover, we found that productivity growth in the industry is dominated establishment data at Census, the results are quite striking and clearly call for further analysis.
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Manufacturing Extension And Productivity Dynamics
June 1998
Working Paper Number:
CES-98-08
This paper presents results from an investigation of the effects of manufacturing extension on the productivity dynamics of client plants. Previous econometric studies of manufacturing extension had very little time series information. This limited what researchers could say about the relative timing of extension services and performance improvements. In turn, this makes it difficult to attribute performance improvements to the receipt of extension services. In this paper, I use a panel of client and nonclient plants to more carefully analyze the dynamics of extension and productivity. The results suggest that the timing of observed productivity improvements at client plants is consistent with a positive impact of manufacturing extension. Estimated program impacts are within the range of those found in previous studies.
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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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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.
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Primary Versus Secondary Production Techniques in U.S. Manufacturing
October 1994
Working Paper Number:
CES-94-12
In this paper we discuss and analyze a classical economic puzzle: whether differences in factor intensities reflect patterns of specialization or the co-existence of alternative techniques to produce output. We use observations on a large cross-section of U.S. manufacturing plants from the Census of Manufactures, including those that make goods primary to other industries, to study differences in production techniques. We find that in most cases material requirements do not depend on whether goods are made as primary products or as secondary products, which suggests that differences in factor intensities usually reflect patterns of specialization. A few cases where secondary production techniques do differ notably are discussed in more detail. However, overall the regression results support the neoclassical assumption that a single, best-practice technique is chosen for making each product.
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The Dynamics Of Productivity In The Telecommunications Equipment Industry
February 1992
Working Paper Number:
CES-92-02
Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant-level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms choices on whether to liquidate and the on the quantities of inputs demanded should they continue depend on their productivity, we develop an estimation algorithm that takes into account the relationship between productivity on the one hand, and both input demand and survival on the other. The algorithm is guided by a dynamic equilibrium model that generates the exit and input demand equations needed to correct for the simultaneity and selection problems. A fully parametric estimation algorithm based on these decision rules would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semiparametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact that there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown, of unproductive plants and by a disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.
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