Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T16:32:44.071Z Has data issue: false hasContentIssue false
  • English
  • Français

A Technique to Estimate Input Productivity from Farm Data

Published online by Cambridge University Press:  28 April 2015

John R. Allison  and
David W. Parvin Jr.
John R. Allison
Affiliation:
Department of Agricultural Economics, Georgia Experiment Station, University of Georgia
David W. Parvin Jr.
Affiliation:
Department of Agricultural Economics, Mississippi State University
Get access Rights & Permissions [Opens in a new window]

Extract

Unfortunately, procedures are not available for handling variations induced by unquantifiable difference in location, soil, weather or management, particularly if the data source is farm survey information from relatively small samples for a single production season. Estimation problems occur regardless of whether classical, profit or trans-log approaches are used. Procedures suggested by Hoch and Hoch and Mundlak for handling these disturbances in classical production functions require a priori knowledge to devise a weighting system or observations over time to provide estimates of weights.

Profit functions as proposed by Lau and Yotopoulos require data to be of such nature that a production function can be specified either in the normal form or that the relationship between profit and input quantities can be specified and estimated. The price of the product is also required to be either a function of quality or of selling costs or that some common or average price is utilized.

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 8 , Issue 2 , December 1976 , pp. 5 - 11
Copyright
Copyright © Southern Agricultural Economics Association 1976

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Binswanger, Hans P. “A Cost Function Approach to the Measurement of Elasticities of Factor Demand and Elasticities of Substition,” American Journal of Agricultural Economics, Volume 56:2 May 1974, pp. 377–385.CrossRefGoogle Scholar
[2] Hoch, Irving. “Simultaneous Equation Bias in the Context of the Cobb-Douglas Production Function,” Econometrica, Volume 26, October 1958, pp. 566–578.CrossRefGoogle Scholar
[3] Johnston, J.Econometrics Methods, 2nd Edition, New York: McGraw-Hill Book Company, 1972.Google Scholar
[4] Law, Lawrence J. and Yotopoulos, Pan A.. “Profit, Supply, and Factor Demand Functions,” American Journal of Agricultural Economics, Volume 54:1, February 1972, pp. 11–18.Google Scholar
[5] Marquardt, D. W. “Generalized Inverses, Ridge Regression, Biased Linear Estimation and Nonlinear Estimation,”Technometrics, Volume 12:3, August 1970, pp. 591–612.CrossRefGoogle Scholar
[6] Mundlak, Yair and Hoch, Irving. “Consequences of Alternative Specifications in Estimation of Cobb-Douglas Production Functions,” Econometrica, Volume 33, October 1965, pp. 814–828.CrossRefGoogle Scholar
[7] Snee, Ronald D. “Some Aspects of Nonorthogonal Data Analysis, Part I. Developing Prediction Equations,” Journal of Quality Technology, Volume 5:2, April 1973.Google Scholar
[8] Spann, Robert M. “Rate of Return Regulation and Efficiency in Production: An Empirical Test of the Averch-Johnson Thesis,” The Bulletin of Economics and Management Sciences, Volume 5:1, Spring 1974, pp. 38–52.Google Scholar