An Appliation Of The Stochastic Latent Variable Approach To The Correction Of Sector Level Tfp Calculations In The Face Of Biased Technological Change
The measurement of the impact of technical change has received significant attention within the economics literature. One popular method of quantifying this impact of technical change is the use of growth accounting index numbers. However, in a recent article Nelson and Pack (1999) criticise the use of such index numbers in situations where technical change is likely to be biased in favour of one or other inputs. In particular they criticise the common approach of applying observed factor shares as proxies for partial output elasticities to weight the change in quantities which they claim are only valid under Hicks neutrality. Recent advances in the measurement of product and factor biases of technical change developed by Balcombe et al (2000) provide a relatively straight-forward means of correcting product and factor shares in the face of biased technical progress. This paper demonstrates the correction of factor shares used in the construction of a TFP index for UK agriculture over the period 1953 to 2000 using both revenue and cost function share equations appended with stochastic latent variables to capture the bias effect. Technical progress is shown to be biased between both individual input and output groups. Output and input quantity aggregates are then constructed using both observed and corrected share weights and the resulting TFPs are compared. There does appear to be some significant bias in TFP if the effect of biased technical progress is not taken into account when constructing the weights.
|Date of creation:||2003|
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- Yougesh Khatri & Colin Thirtle, 1996. "Supply And Demand Functions For Uk Agriculture: Biases Of Technical Change And The Returns To Public R&D," Journal of Agricultural Economics, Wiley Blackwell, vol. 47(1-4), pages 338-354.
- J. Stephen Clark & Curtis E. Youngblood, 1992. "Estimating Duality Models with Biased Technical Change: A Time Series Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(2), pages 353-360.
- Chambers,Robert G., 1988. "Applied Production Analysis," Cambridge Books, Cambridge University Press, number 9780521314275, Junio.
- Balcombe, Kelvin George & Bailey, Alastair & Morrison, Jamie & Rapsomanikis, George & Thirtle, Colin G., 2000. "Stochastic biases in technical change in South African agriculture," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 39(4), December.
- Murgai, Rinku, 2001. "The Green Revolution and the productivity paradox: evidence from the Indian Punjab," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 25(2-3), September.
- Murgai, Rinku, 2001. "The Green Revolution and the productivity paradox: evidence from the Indian Punjab," Agricultural Economics, Blackwell, vol. 25(2-3), pages 199-209, September.
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