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Estimation of costs of technical and allocative inefficiency

Author

Listed:
  • Subal C. Kumbhakar

    (State University of New York
    Inland Norway University of Applied Sciences)

  • Mike G. Tsionas

    (Montelllier Business School
    Lancaster University Management School)

Abstract

If a firm is both technically and allocatively inefficient its cost will increase. Since allocative inefficiency results from misallocation of inputs, cost of allocative inefficiency (CAI) can be obtained if the input over(under) use can be analytically derived. This is only possible for production functions for which input demand functions can be explicitly derived. Schmidt and Lovell (1979) addressed this problem using a Cobb-Douglas production function with the cost minimizing behavior. In this paper, we propose a mixture of Cobb-Douglas (CD) production functions to get a more flexible system that allows obtaining costs of technical and allocative inefficiency analytically. Inflexibility of the CD function is addressed by making its parameters (input elasticities) functions of environmental/predetermined variables. Our empirical evidence show considerable heterogeneity in parameters and multimodal distributions of many quantities of interest, which support our formulation.

Suggested Citation

  • Subal C. Kumbhakar & Mike G. Tsionas, 2021. "Estimation of costs of technical and allocative inefficiency," Journal of Productivity Analysis, Springer, vol. 55(1), pages 41-46, February.
    • Handle: RePEc:kap:jproda:v:55:y:2021:i:1:d:10.1007_s11123-020-00596-4
    DOI: 10.1007/s11123-020-00596-4
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    References listed on IDEAS

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    1. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    2. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    3. Lai, Hung-pin & Kumbhakar, Subal C., 2019. "Technical and allocative efficiency in a panel stochastic production frontier system model," European Journal of Operational Research, Elsevier, vol. 278(1), pages 255-265.
    4. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    5. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    6. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    7. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
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