DEAにおけるDFMモデルを用いた都市行政経営の効率性改善
—— 日本における政令指定都市への適用 ——

抄録

Data Envelopment Analysis (DEA) has become an established approach in the analysis of efficiency problems in both public and private sectors. The aim of this paper is to present a newly developed Distance Friction Minimization (DFM) approach based on the BCC (Banker-Charnes-Cooper) model in order to provide an appropriate efficiency-improving projection model in DEA. In this approach a generalized distance friction will be developed to assist a Decision Making Unit (DMU) in improving its efficiency by a proper movement towards the efficiency frontier surface.
Our DFM model is based on a generalized distance friction function and serves to assist a DMU in improving its performance by a proper movement towards the efficiency frontier surface. Standard DEA models use a uniform input reduction or a uniform output augmentation in the improvement projections, but our DFM approach aims to generate a new contribution to efficiency enhancement strategies by deploying a weighted projection function, while it may address both input reduction and output augmentation as a strategy of a DMU. A suitable form of multidimensional projection functions mapping out efficiency improvement is given by a Multiple Objective Quadratic Programming (MOQP) model in conformity with a Euclidean distance.
The above-mentioned extended DEA model will be empirically illustrated by using a data set on government-ordinance-designated cities in Japan, where the aim is to increase the efficiency of administration management in these cities, based on various input and output performance characteristics of these cities.

JFL classification: C44, C61, H72