Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities

doi:10.1016/S0377-2217(97)00321-4

European Journal of Operational Research

Volume 111, Issue 1, 16 November 1998, Pages 50-61

https://doi.org/10.1016/S0377-2217(97)00321-4 Get rights and content

Abstract

This article compares two approaches in aggregating multiple inputs and multiple outputs in the evaluation of decision making units (DMUs), data envelopment analysis (DEA) and principal component analysis (PCA). DEA, a non-statistical efficiency technique, employs linear programming to weight the inputs/outputs and rank the performance of DMUs. PCA, a multivariate statistical method, combines new multiple measures defined by the inputs/outputs. Both methods are applied to three real world data sets that characterize the economic performance of Chinese cities and yield consistent and mutually complementary results. Nonparametric statistical tests are employed to validate the consistency between the rankings obtained from DEA and PCA.

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Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities

Abstract

European Journal of Operational Research

Socio-Economic Planning Science

Journal of Econometrics

European Journal of Operational Research

A procedure for ranking efficient units in data envelopment analysis

Management Science

A comparative application of data envelopment analysis and translog methods: An illustrative study of hospital production

Management Science

Multivariate Statistical Methods for Business and Economics