Abstract
The aim of this chapter is to introduce essential features of data envelopment analysis (DEA) for regional performance measurement. We introduce basic rules of application to regional performance measurement from the viewpoint of economies of scale, treatment of input and output items, and the number of DMUs, inputs and outputs. Furthermore, in applications of DEA, we usually need to pay attention to the influence on efficiency scores by the number of DMUs, inputs and outputs. This requires an in-depth understanding, not only of the impact of the number of items or DMUs on efficiency scores but also of the influence of the quality of items on efficiency scores. This chapter focuses especially on the abovementioned critical issues; we present sensitivity analysis results of efficiency scores regarding the number of DMUs, input items and output items, based on a case study that uses input and output data on the critical energy-environment-economic (EEE) variables for a set of 47 prefectures in Japan.
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Ali, A., & Seiford, L. M. (1993). The mathematical programming approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 120–159). New York: Oxford University Press.
Anderson, P., & Petersen, N. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261–1264.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.
Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses. Inc: Springer Science + Business Media.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software. New York: Springer Science + Business Media.
Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259.
Seiford, L. (2005). A cyber-bibliography for data envelopment analysis (1978–2005). CD-ROM in introduction to data envelopment analysis and its uses. New York: Springer Science Business Media.
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Suzuki, S., Nijkamp, P. (2017). Significance of DEA for Regional Performance Measurement. In: Regional Performance Measurement and Improvement. New Frontiers in Regional Science: Asian Perspectives, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-0242-7_3
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DOI: https://doi.org/10.1007/978-981-10-0242-7_3
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