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A Generalized Goals-achievement Model in Data Envelopment Analysis


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  • Soushi Suzuki

    (Hokkai-Gakuen University, Sapporo, Japan)

  • Peter Nijkamp

    (VU University Amsterdam)


Data Envelopment Analysis (DEA) has become an established tool in comparative analyses of efficiency strategies in both the public and the private sector. The aim of this paper is to present and apply a newly developed, adjusted DEA model – emerging from a blend of a Distance Friction Minimization (DFM) and a Goals Achievement (GA) approach on the basis of the Charnes-Cooper-Rhodes (CCR) method – in order to generate a more satisfactory efficiency-improving projection model in conventional DEA. Our DFM model is based on a generalized Euclidean distance minimization and serves to assist a Decision Making Unit (DMU) in improving its performance by the most appropriate movement towards the efficiency frontier surface. Our DFM approach aims to generate a new contribution to efficiency enhancement strategies by deploying a weighted projection function. In addition, it may address both input reduction and output increase as a strategy of a DMU. The GA model can compute the input reduction value or the output increase value in order to achieve a pre-specified goal value for the efficiency improvement in an optimal way. The above-mentioned DFM-GA model is illustrated empirically by using a data set of efficiency indicators for cities in Hokkaido prefecture in Japan, where the aim is to increase the efficiency of local government finance mechanisms in these cities, based on various input and output performance characteristics.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-110/3.

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Date of creation: 13 Nov 2008
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Handle: RePEc:dgr:uvatin:20080110

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Keywords: Distance Friction Minimization; Goals Achievement; Data Envelopment Analysis (DEA); Efficiency-improving Projection; Local Government Finance;

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  1. Doyle, J & Green, R, 1993. "Data envelopment analysis and multiple criteria decision making," Omega, Elsevier, Elsevier, vol. 21(6), pages 713-715, November.
  2. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, INFORMS, vol. 30(9), pages 1078-1092, September.
  3. M. Halme & T. Joro & P. Korhonen & S. Salo & J. Wallenius, 1998. "Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis," Working Papers, International Institute for Applied Systems Analysis ir98054, International Institute for Applied Systems Analysis.
  4. Tarja Joro & Pekka Korhonen & Jyrki Wallenius, 1998. "Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming," Management Science, INFORMS, INFORMS, vol. 44(7), pages 962-970, July.
  5. P. Korhonen, 1998. "Multiple Objective Programming Support," Working Papers, International Institute for Applied Systems Analysis ir98010, International Institute for Applied Systems Analysis.
  6. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, INFORMS, vol. 45(1), pages 103-115, January.
  7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, Elsevier, vol. 2(6), pages 429-444, November.
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