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한국 물류산업의 효율성과 생산성: 비모수적 기법과 모수적 기법의 적용
김창범 ( Chang Beom Kim ) 한국해운물류학회(구 한국해운학회) 2015 해운물류연구 Vol.31 No.3
본고는 우리나라 물류산업에 대해 DEA기법을 적용하여 효율성과 생산성을 분석하고, 패널 SFA기법과 패널 Tobit모형을 적용하여 규모의 수익 패턴과 효율성의 결정요인을 분석하였다. 첫째, CCR기준 초효율성과 SBM기준 초효율성 분석 결과 육상운송업이 가장 비효율적이며, 수상운송업이 가장 효율적인 것으로 나타났다. 또한 업종별로는 외항화물운송업이 가장 효율적이며, 도시철도운송업이 가장 비효율적인 것으로 나타났다. 또한 SFA기법을 적용한 결과 생산의 노동탄력성과 자본탄력성이 1에 근접하여 규모의 수익불변 특징을 보여주었다. 둘째, 패널 Tobit분석 결과 1인당 생산성 향상은 효율성에 긍정적인 영향을 미쳤으며, 1인당 자본량과 평균비용 증가는 효율성에 부정적인 영향을 미쳤다. 셋째, DEA/Window 기법으로 효율성의 동태적 안정성 여부를 살펴본 결과, 도선업이 가장 불안정적이며 도시철도운송업이 가장 안정적이었다. 넷째, Malmquist 생산성 분석을 통해 생산성 증가와 감소의 가장 큰 요인은 각각 기술진보와 기술퇴보로 분석되었다. The main purpose of this paper is to investigate the efficiency and productivity of 40 logistics industries in Korea using a variety of method: super CCR, super SBM, DEA window analysis, and the Malmquist productivity index. The super efficiency model is a method for selecting the most efficient of all efficient decision making units (DMUs). The super SBM (slack-based measure of super-efficiency) model was introduced as an alternative method to calculating the super efficiency score without considering the slacks of input variables and output variables. Using these models, the most efficient DMU has a value greater than one. The Malmquist productivity index has many attractive features. For one thing, it decomposes the result into a technical efficiency change index and frontier change index, allowing the productivity change to be attributed to either changes in technical efficiency or changes in technology(i.e., technological progress in the industry), or both. The total factor productivity change is the product of technical efficiency change and technological change. Technical efficiency change can be decomposed into pure technical efficiency and scale efficiency change. Window analysis is one of methods used to verify productivity change over time and works on the principle of moving averages. DEA window analysis is used to measure efficiency in cross-sectional and time variant data. Thus, it is useful for detecting performance trends for a DMU over time. Each DMU is treated as a different industry in a different period, which allows for increasing the number of data points. In other words, each DMU in a different period is treated as if it were an independent DMU but remains comparable in the same window. The advantage of DEA window analysis is that the performance of an industry in a given period can be compared to itself and to other industries over time. The empirical results are as follows: First, by both the super CCR efficiency and super SBM efficiency methods, inefficiency of land transport and water transport was indicated as 67-74% and 30-44% respectively. These results indicate that land is the most inefficient form of transport, whereas water transport is the most efficient. The annual efficiency values revealed that the degree of inefficiency has increased since 2011. Also, detailed results for sectors show that ocean shipping is the most efficient way to transport cargo, whereas urban rail transport is the most inefficient. Second, tobit panel analysis showed that labor productivity has positive impact on efficiency, whereas average cost has negative impact on efficiency. Third, the DEA window analysis results calculated using LDP values and standard deviations show that ferry transport is the most stable, whereas urban railway transport is the most unstable. Fourth, Malmquist productivity index results show that productivity improved 1.6% on average, with technological progress being the major factor decreasing productivity. These results indicate that it is necessary to expand the logistics market to increase the efficiency of the industry. To do so, many efforts must be made to global leadership in logistics enterprises, invest on global logistics infrastructure, increase research and development, and expand the future use of new technologies.