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A generalized goals-achievement model in data envelopment analysis : an application to efficiency improvement in local government finance in Japan

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  • Suzuki, S.

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Nijkamp, P.

Abstract

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. Standard DEA models use a uniform proportial input reduction or a uniform proportial output increase in the improvement projections, but our DFM approach aims to generate a new contribution to efficiency enhancement strategies by deploying a weighted projection function. In addition, at the same time, it may address both input reduction and output increase as a strategy of a DMU. A suitable form of multidimensional projection functions that serves to improve efficiency is given by a Multiple Objective Quadratic Programming (MOQP) model using a Euclidean distance. Another novelty of our approach is the introduction of prior goals set by a DMU by using a GA approach. The GA model specifies a goal value for efficiency improvement in a DFM model. 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. Next, using the integrated DFM- GA model, we are able to develop an operational efficiency-improving projection that provides a clear, quantitative orientation for the actions of a DMU.

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Bibliographic Info

Paper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0014.

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Date of creation: 2008
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Handle: RePEc:dgr:vuarem:2008-14

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Web page: http://www.feweb.vu.nl

Related research

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

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  1. Tarja Joro & Pekka Korhonen & Jyrki Wallenius, 1998. "Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming," Management Science, INFORMS, vol. 44(7), pages 962-970, July.
  2. P. Korhonen, 1998. "Multiple Objective Programming Support," Working Papers ir98010, International Institute for Applied Systems Analysis.
  3. Doyle, J & Green, R, 1993. "Data envelopment analysis and multiple criteria decision making," Omega, Elsevier, vol. 21(6), pages 713-715, November.
  4. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
  5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  6. M. Halme & T. Joro & P. Korhonen & S. Salo & J. Wallenius, 1998. "Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis," Working Papers ir98054, International Institute for Applied Systems Analysis.
  7. Thanassoulis, E. & Dyson, R. G., 1992. "Estimating preferred target input-output levels using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 56(1), pages 80-97, January.
  8. 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, vol. 45(1), pages 103-115, January.
  9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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Cited by:
  1. Laura Carosi & Giovanna D’Inverno & Letizia Ravagli, 2014. "Global public spending efficiency in Tuscan municipalities," Discussion Papers 2014/175, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
  2. Peter Bönisch & Peter Haug & A. Illy & L. Schreier, 2011. "Municipality Size and Efficiency of Local Public Services: Does Size Matter?," IWH Discussion Papers 18, Halle Institute for Economic Research.
  3. Karima Kourtit & Daniel Arribas-Bel & Peter Nijkamp, 2012. "High performers in complex spatial systems: a self-organizing mapping approach with reference to The Netherlands," The Annals of Regional Science, Springer, vol. 48(2), pages 501-527, April.

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