Papers Containing Keywords(s): 'industries estimate'
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Viewing papers 11 through 12 of 12
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Working PaperThe Role of Technological and Industrial Heterogeneity In Technology Diffusion: a Markovian Approach
February 2003
Working Paper Number:
CES-03-07
Recent empirical studies have established the importance of intra and inter-industry heterogeneity in investment in innovation and other outcomes. This paper examines the role of industry and technology heterogeneity in the diffusion of advanced manufacturing technologies from a simple Markovian approach. Using the Maximum Entropy estimator, I estimate transition probabilities and corresponding half-lives, look for outliers in technology and industry diffusion patterns, and try to find explanations of their unusual behavior in idiosyncratic technology and industry characteristics. A consistent industry-level pattern that emerged is one that relates consumer demand and production processes. It seems that in industries where hand-made products are a sign of quality to the customer, technology spreads very slowly. On the other hand, in industries where demand for sophisticated, high-precision goods is high or in industries where demand-driven product specifications vary quite rapidly over relatively short periods of time, advanced technologies diffuse much more rapidly.View Full Paper PDF
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Working PaperThe 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.View Full Paper PDF
