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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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Data Envelopment Analysis (DEA) computes a scalar measure of efficiency and determines efficient levels of inputs and outputs for the organizations under evaluation. This paper applies Two Data Envelopment Analysis (DEA) models to 76 (public or private) solid waste collection organizations in Seoul. The models include a Constant Returns to Scale (CRS) model and a Variable Returns Scale (VRS) model.
The first thing this paper finds is that the mean technical efficiency for private organizations is 69.5% while the mean technical efficiency for public organizations is 60.7% under a CRS model. Secondly, under a VRS model, the mean technical efficiency for private organizations is 83.9% while the mean technical efficiency for public organizations is 76.8%. Further, DEA and regression models are joined together in a two-stage complementary manner. DEA first identifies efficient and inefficient organizations and regression models in tum incorporate this information in the form of dummy variables. This paper finds this approach give very satisfactory results Major policy recommendations are as follows. First, contracting out all public solid waste collection organizations may lead to a loss of efficiency since some public DMUs operate in a very efficient manner while the opposite is true for some private DMUs. Second, two adjacent public organizations could be merged into one entity to enhance technical efficiency. This implies that each the local autonomous ward in Seoul do not necessarily operate its own solid waste collection organization. Finally, prior to contracting out public solid waste collection organizations, some measures should be taken to boost efficient operations of private solid waste collection organizations. This includes the merger of small private solid waste collection organizations.
The first thing this paper finds is that the mean technical efficiency for private organizations is 69.5% while the mean technical efficiency for public organizations is 60.7% under a CRS model. Secondly, under a VRS model, the mean technical efficiency for private organizations is 83.9% while the mean technical efficiency for public organizations is 76.8%. Further, DEA and regression models are joined together in a two-stage complementary manner. DEA first identifies efficient and inefficient organizations and regression models in tum incorporate this information in the form of dummy variables. This paper finds this approach give very satisfactory results Major policy recommendations are as follows. First, contracting out all public solid waste collection organizations may lead to a loss of efficiency since some public DMUs operate in a very efficient manner while the opposite is true for some private DMUs. Second, two adjacent public organizations could be merged into one entity to enhance technical efficiency. This implies that each the local autonomous ward in Seoul do not necessarily operate its own solid waste collection organization. Finally, prior to contracting out public solid waste collection organizations, some measures should be taken to boost efficient operations of private solid waste collection organizations. This includes the merger of small private solid waste collection organizations.
목차
ABSTRACT
Ⅰ. 서론
Ⅱ. 자료포락분석(Data Envelopment Analysis)을 이용한 효율성 분석
Ⅲ. 모형 및 자료
Ⅳ. 직영청소조직의 효율성 분석 결과
Ⅴ. 결론
참고문헌
〈부록〉
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