Skip to main content

Stochastic Frontier Production Functions and Technical Efficiency Measurements: A Review

  • Chapter
Productivity and Growth in Chinese Agriculture

Part of the book series: Studies on the Chinese Economy ((STCE))

Abstract

Measuring the productive performance of economic decision-making units has assumed importance in recent years, particularly due to globalisation and the opening up of several socialist countries and developing economies. Generally, economic efficiency and its two major components — technical and allocative efficiency — are used as core measures of performance. Technical efficiency is defined as the ability and willingness of firms to produce the maximum possible output with a specified quantity of inputs, given the prevailing technology and environmental conditions. In other words, a firm is said to be technically efficient, if it is able to realise the full potential of its technology with a given set of inputs.1 Allocative efficiency is defined as the ability and willingness to use the quantity of inputs that will maximise net revenue (profit), given the current conditions of factor supply and market demand. An economic decision-making unit is said to be economically efficient when it is both technically and allocatively efficient.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Aigner, D. J., C. A. K. Lovell and P. Schmidt (1977) ‘Formulation and estimation of stochastic frontier production function models’, Journal of Econometrics, Vol. 6, pp. 21–37.

    Article  Google Scholar 

  • Banker, R. D. and A. Maindiratta (1988) ‘Nonparametric analysis of technical and allocative efficiencies in production’, Econometrica, vol. 56, pp. 1315–32.

    Article  Google Scholar 

  • Battese, G. E. and T. J. Coelli (1988) ‘Prediction of firm-level technical efficiencies with a generalised frontier production function and panel data’, Journal of Econometrics, vol. 38, pp. 387–9.

    Article  Google Scholar 

  • Battese, G. E. and T. J. Coelli (1992), ‘Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India’, Journal of Productivity Analysis, no. 3, pp. 153–69.

    Article  Google Scholar 

  • Bauer, P. W. (1990) ‘Recent developments in the econometric estimation of frontiers’, Journal of Econometrics, vol. 46, pp. 39–56.

    Article  Google Scholar 

  • Breusch, T. S. and A. R. Pagan (1979) ‘A simple test of heteroscedasticity and random coefficient variation’, Econometrica, vol. 47, pp. 1287–94.

    Article  Google Scholar 

  • Caudill, S. B. and J. M. Ford (1993) ‘Biases in frontier estimation due to heteroscedasticity’, Economics Letters, vol. 41, pp. 17–20.

    Article  Google Scholar 

  • Charnes, A., W. W. Cooper and E. Rhodes (1978) ‘Measuring the efficiency of decision making units’, European Journal of Operations Research, vol. 2, pp. 429–49.

    Article  Google Scholar 

  • Coelli, T. J. (1992) ‘A computer program for frontier production function estimation’, Economics Letters, vol. 37, pp. 29–32.

    Article  Google Scholar 

  • Cornwell, C., P. Schmidt and R. C. Sickles (1990) ‘Production frontiers with cross-sectional and time-series variation in efficiency levels’, Journal of Econometrics, vol. 46, pp. 185–200.

    Article  Google Scholar 

  • Debreu, G. (1951), ‘The coefficient of resource utilisation’, Econometrica, vol. 19, pp. 273–92.

    Article  Google Scholar 

  • Farrell, M. J. (1957) ‘The measurement of productive efficiency’, Journal of the Royal Statistical Society, Series A, vol. 120, pp. 253–81.

    Article  Google Scholar 

  • Fan, S. (1991) ‘Effects of technological change and institutional reform on production growth in Chinese agriculture’, American Journal of Agricultural Economic, vol. 73, pp. 266–75.

    Article  Google Scholar 

  • Griffiths, W. E. (1972) ‘Estimation of actual response coefficients in the Hildreth-Houck random coefficient model’, Journal of the American Statistical Association, vol. 67, pp. 633–5.

    Article  Google Scholar 

  • Hausman, J. A. and W. E. Taylor (1981) ‘Panel data and unobservable individual effects’, Econometrica, vol. 49, pp. 1377–99.

    Article  Google Scholar 

  • Hildreth, C. and J. P. Houck (1968) ‘Some estimators for a model with random coefficients’, Journal of American Statistical Association, vol. 63, pp. 584–95.

    Google Scholar 

  • Jondrow, J., C. A. K. Lovell, I. S. Materov and P. Schmidt (1982) ‘On the estimation of technical inefficiency in the stochastic frontier production function model’, Journal of Econometrics, vol. 19, pp. 233–8.

    Article  Google Scholar 

  • Kalirajan, K. P. and J. C. Flinn (1983) ‘The measurement of farm-specific technical efficiency’, Pakistan Journal of Applied Economics, vol. 2, pp. 67–180.

    Google Scholar 

  • Kalirajan, K. P. and M. Obwona (1994) ‘Frontier production function: a stochastic coefficients approach’, Oxford Bulletin of Economics and Statistics, vol. 56, pp. 85–94.

    Google Scholar 

  • Kalirajan, K. P. and R. T. Shand (1994) Economics in Disequilibrium: An Approach from the Frontier (New Delhi: Macmillan).

    Google Scholar 

  • Kalirajan, K. P., M. B. Obwona and S. Zhao (1996) ‘A decomposition of total factor productivity growth: The case of Chinese agricultural growth before and after reform’, American Journal of Agricultural Economics, vol. 78, pp. 331–38.

    Article  Google Scholar 

  • Koopmans, T. C. (1951) ‘An analysis of production as an efficient combination of activities’, in T. C. Koopmans (ed.), Activity Analysis of Production and Allocation. Cowles Commission for Research in Economics Monograph no. 13 (New York: John Wiley & Sons).

    Google Scholar 

  • Kopp, R. J. and J. Mullahy (1990) ‘Moment-based estimation and testing of stochastic frontier models’, Journal of Econometrics, vol. 46, pp. 165–83.

    Article  Google Scholar 

  • Kullback, S. (1958) Information Theory and Statistics (New York: John Wiley & Sons).

    Google Scholar 

  • Kumbhakar, S. C. (1990) ‘Production frontiers, panel data and time varying technical inefficiency’, Journal of Econometrics, vol. 46, pp. 201–11.

    Article  Google Scholar 

  • Meeusen, W. and J. van den Broeck (1977) ‘Efficiency estimation from Cobb-Douglas production functions with composed error’, International Economic Review, vol. 18, pp. 435–44.

    Article  Google Scholar 

  • Nishimizu, M. and J. M. Page (1982) ‘Total factor productivity growth, technological progress and technical efficiency changes: Dimension of productivity change in Yugoslavia’, Economic Journal, vol. 92, pp. 920–36.

    Article  Google Scholar 

  • Schmidt, P. (1986) ‘Frontier production functions’, Econometric Reviews, vol. 4, pp. 289–328.

    Article  Google Scholar 

  • Sengupta, J. K. (1989) Efficiency Analysis by Production Frontiers: the Nonparametric Approach (Dordrecht: Kluwer).

    Google Scholar 

  • Stevenson, R. E. (1980) ‘Likelihood functions for generalised stochastic frontier estimation’, Journal of Econometrics, vol. 13, pp. 57–66.

    Article  Google Scholar 

  • Swamy, P. A. V. B. (1970) ‘Efficient inference in a random coefficient regression model’, Econometrica, vol. 38, pp. 311–23.

    Article  Google Scholar 

  • Van den Broeck, J., F. Broeck, F. and L. Kaufman (1991), ‘Decomposing stochastic frontier efficiency into secular and organisatorial efficiency’, Journal of Computational and Applied Mathematics, vol. 37, pp. 251–64.

    Article  Google Scholar 

  • Van den Broeck, J., G. Koop, J. Osiewalski and M. F. J. Steel (1994) ‘Stochastic frontier models: a Bayesian perspective’, Journal of Econometrics, vol. 61, pp. 273–303.

    Article  Google Scholar 

  • Waldman, M. (1984) ‘Properties of technical efficiency estimations in the stochastic frontier model’, Journal of Econometrics, vol. 24, pp. 346–53.

    Google Scholar 

  • Wu, Y. (1996) Productive Performance of Chinese Enterprises: An empirical analysis (London: Macmillan).

    Book  Google Scholar 

  • Zhao, S. (1994) ‘Economic reforms and efficiency of Chinese state enterprises: with special reference to energy input use’, PhD thesis submitted to the Australian National University.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Copyright information

© 1999 Palgrave Macmillan, a division of Macmillan Publishers Limited

About this chapter

Cite this chapter

Kalirajan, K.P., Shand, R. (1999). Stochastic Frontier Production Functions and Technical Efficiency Measurements: A Review. In: Kalirajan, K.P., Wu, Y. (eds) Productivity and Growth in Chinese Agriculture. Studies on the Chinese Economy. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-27448-2_2

Download citation

Publish with us

Policies and ethics