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The Long-Run Demand for Labor: Estimates From Census Establishment Data
September 1993
Working Paper Number:
CES-93-13
This paper estimates long-run demand functions for production workers, production worker hours, and nonproduction workers using micro data from U.S. establishment surveys. The paper focuses on estimation of the wage and output elasticities of labor demand using data on over 41,000 U.S. manufacturing plants in 1975 and more than 30,000 plants in 1981. Particular attention is focused on the problems of unobserved producer heterogeneity and measurement errors in output that can affect labor demand estimates based on establishment survey data. The empirical results reveal that OLS estimates of both the own-price elasticity and the output elasticity of labor demand are biased downward as a result of unobserved heterogeneity. Differencing the data as a solution to this problem greatly exaggerates measurement error in the output coefficients. The use of capital stocks as instrumental variables to correct for measurement error in output significantly alters output elasticities in the expected direction but has no systematic effect on own-price elasticities. All of these patterns are found in estimates that pool establishment data across industries and in industry-specific regressions for the vast majority of industries. Estimates of the output elasticity of labor demand indicate that there are slight increasing returns for production workers and production hours, with a pooled data estimate of .92. The estimate for nonproduction workers in .98. The variation in the output elasticities across industries is fairly small. Estimates of the own-price elasticity vary more substantially with the year, type of differencing used, and industry. They average -.50 for production hours, -.41 for production workers, and -.44 for nonproduction workers. The price elasticities vary widely across manufacturing industries: the interquartile range for the industry estimates is approximately .40.
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Evidence on IO Technology Assumptions From the Longitudinal Research Database
May 1993
Working Paper Number:
CES-93-08
This paper investigates whether a popular IO technology assumption, the commodity technology model, is appropriate for specific United States manufacturing industries, using data on product composition and use of intermediates by individual plants from the Census Longitudinal Research Database. Extant empirical research has suggested the rejection of this model, owing to the implication of aggregate data that negative inputs are required to make particular goods. The plant-level data explored here suggest that much of the rejection of the commodity technology model from aggregative data was spurious; problematic entries in industry-level IO tables generally have a very low Census content. However, among the other industries for which Census data on specified materials use is available, there is a sound statistical basis for rejecting the commodity technology model in about one-third of the cases: a novel econometric test demonstrates a fundamental heterogeneity of materials use among plants that only produce the primary products of the industry.
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Environmental Regulation And Manufacturing Productivity At The Plant Level
March 1993
Working Paper Number:
CES-93-06
This paper presents results for an analysis of plant-level data from three manufacturing industries (paper, oil, and steel). We combine productivity data from the Longitudinal Research Database ( LRD ) with pollution abatement expenditures from the Census Bureau's Pollution Abatement Cost and Expenditures (PACE) survey, as well as regulatory measures taken from datasets maintained by the Environmental Protection Agency. We use data from 1979 to 1985, considering both labor and total factor productivity, both levels and growth rates, and both annual measures and averages over the period. We find a strong connection between regulation and productivity when regulation is measured by compliance costs. More regulated plants have significantly lower productivity levels and slower productivity growth rates than less regulated plants. The magnitude of the impacts are larger than expected: a $1 increase in compliance costs appears to reduce TFP by the equivalent of $3 to $4. Thus, commonly used methods of calculating the impact of regulation on productivity are substantially underestimated. These results are generally consistent across industries and for different estimation methods. Our other measures of regulation (compliance status, enforcement activity, and emissions) show much less consistent results. Higher enforcement, lower compliance, and higher emissions are generally associated with lower productivity levels and slower productivity growth, but the coefficients are rarely significant.
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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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Manufacturing Establishments Reclassified Into New Industries: The Effect Of Survey Design Rules
November 1992
Working Paper Number:
CES-92-14
Establishment reclassification occurs when an establishment classified in one industry in one year is reclassified into another industry in another year. Because of survey design rules at the Census Bureau these reclassifications occur systematically over time, and affect the industry-level time series of output and employment. The evidence shows that reclassified establishments occur most often in two distinct years over the life of a sample panel. Switches are not only numerous in these years, they also contribute significantly to measured industry change in industry output and employment. The problem is that reclassifications are not necessarily processed in the year that they occur. The survey rules restrict most change to certain years. The effect of these rules is evidenced by looking at the variance across industry growth rates which increases greatly in these two years. Whatever the reason for reclassifying an establishment, the way the switches are processed raises the possibility of measurement errors in the industry level statistics. Researchers and policymakers relying upon observations in annual changes in industry statistics should be aware of these systematic discontinuities, discrepancies and potential data distortions.
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Estimating Capital Efficiency Schedules Within Production Functions
May 1992
Working Paper Number:
CES-92-04
The appropriate method for aggregating capital goods across vintages to produce a single capital stock measure has long been a contentious issue, and the literature covering this topic is quite extensive. This paper presents a methodology that estimates efficiency schedules within a production function, allowing the data to reveal how the efficiency of capital goods evolve as they age. Specifically we insert a parameterized investment stream into the position of a capital variable in a production function, and then estimate the parameters of the production function simultaneously with the parameters of the investment stream. Plant level panel data for a select group of steel plants employing a common technology are used to estimate the model. Our primary finding is that when using a simple Cobb Douglas production function, the estimated efficiency schedules appear to follow a geometric pattern, which is consistent with the estimates of economic depreciation of Hulten and Wykoff (1981). Results from more flexible functional forms produced much less precise and unreliable estimates.
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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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Technical Inefficiency And Productive Decline In The U.S. Interstate Natural Gas Pipeline Industry Under The Natural Gas Policy Act
October 1991
Working Paper Number:
CES-91-06
The U.S. natural gas industry has undergone substantial change since the enactment of the Natural Gas Policy Act of 1978. Although the major focus of the NGPA was to initiate partial and gradual price deregulation of natural gas at the well-head, the interstate transmission industry was profoundly affected by changes in the relative prices of competing fuels and contractual relationships among producers, transporters, distributors, and end-users. This paper assesses the impact of the NGPA on the technical efficiency and productivity of fourteen interstate natural gas transmission firms for the period 1978-1985. We focus on the distortionary effects that resulted in the industry during a period in which changes in regulatory policy could neither anticipate changing market conditions nor rapidly adjust to those changes. Two alternative estimating methodologies, stochastic frontier production analysis and data envelopment analysis, are used to measure the firm-specific and temporal distortionary effects. Concordant findings from these alternative methodologies suggest a pervasive pattern of declining technical efficiency in the industry during the period in which this major regulatory intervention was introduced and implemented. The representative firms experience an average annual decline in efficiency of .55 percent over the sample period. In addition, it appears that the industry suffered a decline in productivity during the sample period, averaging -1.18 percent annually.
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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.
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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.
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