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Title: Technical Change in the North American Forestry Sector: A Review
Author: Stier, Jeffery C.; Bengston, David N.
Publication: Forest Science 38(1):134-159
Abstract: Economists have examined the impact of technical change on the forest products sector using the historical, index number, and econometric approaches. This paper reviews econometric analyses of the rate and bias of technical change, examining functional form, factors included, and empirical results. Studies are classified as first- second-, or third-generation approaches. First-generation studies are based on simple value-added measures of output, usually employ simple functional forms to represent the production technology, and incorporate only capital and labor inputs. Second-generation studies are characterized by estimation of more complex, flexible dual cost or profit functions and typically include resource and often energy inputs. Third-generation studies also rely upon dual formulations of the production structure and include multiple factors; however, in addition they specify the dynamics of adjustment of quasi-fixed factors over time. The studies reveal a tradeoff between the richness of the theoretical structure of the production technology and the consistency of the empirical results. Most studies have reported a labor-saving and energy-using bias to technical change, but little or no wood-saving bias, and many report a wood-using bias. Since technical advances occur in spurts, the use of a simple linear time trend to represent the state of technology is a major limitation of virtually all models. Alternative measures, such as the power ratings or throughput measures, might better capture the characteristics of a technology. Use of such measures might also be combined with a cross-sectional approach in an attempt to avoid some of the statistical problems, such as serial correlation, that characterize time series data. Finally, if the goal of the analysis is to forecast factor demand and cost implications of technical change, simulation models may offer a more promising alternative.
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