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A Distance Function Approach for Estimating Technical and Allocative Inefficiency

Author

Listed:
  • G. Karagiannis

    (Department of International and European Economic & Political Studies, University of Macedonia, Thessaloniki 540 06, Greece)

  • S. C. Kumbhakar

    (Department of Economics, State University of New York, Binghamton, NY 13902, USA.)

  • E. G. Tsionas

    (Department of Economics, Athens University of Economics and Business, Athens, Greece.)

Abstract

This paper presents a procedure for measuring technical and allocative inefficiency in an input distance function framework. The procedure involves solving a system of equations to compute the unobserved cost minimizing input vector that is used to compute the cost of allocative inefficiency. The system is non-linear but a rather simple linear approximation may also be used. Thus we show that the input distance function approach provides information not only on the extent of input over - (under-) utilization but also their impact on cost.

Suggested Citation

  • G. Karagiannis & S. C. Kumbhakar & E. G. Tsionas, 2004. "A Distance Function Approach for Estimating Technical and Allocative Inefficiency," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 39(1), pages 19-30, January.
    • Handle: RePEc:dse:indecr:v:39:y:2004:i:1:p:19-30
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    Citations

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    Cited by:

    1. Tim Coelli & Gholamreza Hajargasht & C.A. Knox Lovell, 2008. "Econometric Estimation of an Input Distance Function in a System of Equations," CEPA Working Papers Series WP012008, School of Economics, University of Queensland, Australia.
    2. Li, Hongchang & Yu, Kemei & Wang, Kun & Zhang, Anming, 2019. "Market power and its determinants in the Chinese railway industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 261-276.

    More about this item

    Keywords

    Technical and Allocative Efficiency; Input Distance Function;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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