• Journal of Korea Port Economic Association
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• v.22 no.3
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• pp.59-78
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• 2006

The purpose of this paper is to analyze the trend on scale efficiency of the Port of Gwangyang from 1994 to 2004 using CCR, BCC, and Malmquist index approaches. The main results are as follows. first, scale efficiency shows a 50% similar [5(94/95, 95/96, 97/98, 2001/2002, 2003/2004) out of 10] pattern to technical efficiency change. Second, total factor productivity increased at 48.57% rate of growth on average in 6 out of 10 periods except 96/97, 97/98, 99/2000, and 2000/2001. 2003/2004 period is the one period experiencing rapid total factor productivity changes, mainly due to technical progress. Third, the ranking order of accumulative indices is scale efficiency change, TFP change, efficiency change, technical change, and pure efficiency change. The main policy implication of this paper is that according to the CCR, BCC, and Malmquist results, the Port of Gwangyang should develop the plan for enhancing the 5 Malmquist indices with following the management way of benchmarking ports.

• Journal of Distribution Science
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• v.11 no.8
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• pp.5-14
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• 2013

Purpose - We live in a world of constant change and competition. Many airports have specific competitiveness goals and strategies for achieving and maintaining them. The global economic recession, financial crises, and rising oil prices have resulted in an increasingly important role for facility investment and renewal and the implementation of appropriate policies in ensuring the competitive advantage for airports. It is thus important to analyze the factors that enhance efficiency and productivity for an airport. This study aims to determine the efficiency levels of 20 major airports in East Asia, Europe, and North America. Further, this study also suggests suitable policies and strategies for their development. Research design, data, and methodology - This paper employs the DEA-CCR, DEA-BCC, and DEA-Malmquist production index analysis models to determine airport efficiency. The study uses data on the efficiency and productivity of the world's leading airports between 2006 and 2010. The input variables include the airport size, the number of runways, the size of passenger terminals, and the size of cargo terminals. The output variables include the annual number of passengers and the annual cargo volume. The study uses basic data from the 2010 World Airport Traffic Report (ACI). The world's top 20 airports (as rated by the ACI report) are investigated. The study uses the expanded DEA Model and the Super Efficiency Model to identify the most effective airports among the top 20. The Malmquist productivity index analysis is used to measure airport effectiveness. Results - This study analyzes longitudinal and cross-sectional data on the world's top 20 airports covering 2006 to 2010. A CCR analysis shows that the most efficient airports in 2010 were Gatwick Airport (LGW), Zurich Airport (ZRH), Vienna Airport (VIE), Leonardo da Vinci Fiumicino Airport (FCO), Los Angeles International Airport (LAX), Seattle-Tacoma Airport (SEA), San Francisco Airport (SFO), HongKong Airport (HKG), Beijing Capital International Airport (PEK), and Shanghai Pudong Airport (PVG). We find that changes in airport productivity are affected more by technical factors than by airport efficiency. Conclusions - Based on the study results, we offer four airport development proposals. First, a benchmark airport needs to be identified. Second, inefficiency must be reduced and high-cost factors need to be managed. Third, airport operations should be enhanced through technical innovation. Finally, scientific demand forecasting and facility preparation must become the focus of attention. This paper has some limitations. Because the Malmquist productivity index is based on the hypothesis of the, the identified production change could be over- or under-estimated. Further, as DEA estimates the relative efficiency. It also cannot generalize to include all airport conditions because the variables are limited. To measure airport productivity more accurately, other input variables and environmental variables such as financial and policy factors should be included.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.35-47
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• 2004

Using data envelopment analysis, this paper annually estimates Malmquist total factor productivity indices and decomposes them into productive efficiency change, technical change, and scale change components for three publicly-owned rail transit properties: the Seoul Subway Corporation(SSC), the Seoul Metropolitan Electrified Railways Sector of Korea National Railroad (SMESRS) and the Busan Urban Transit Authority (BUTA). The paper then conceptualizes that the property produces a single output(car-kilometers) using four inputs(labor, electricity, car and maintenance, and track) and uses unbalanced panel data consisted of annual observations on SSC, SMESRS and BUTA. The results show that the average annual growth rate of productivity of the three properties is 6.6 percent, which is 0.5 percent less than the average annual increasing rate of their labor price. They also show that the greatest part of the growth in productivity is explained by technical change and to a lesser degree by scale change and changes in productive efficiency though each of the three components contributes more than 20 percent to the growth in productivity, These results suggest that the three properties should base the increasing rate of their wages on the growth rate of their productivity and utilize existing technologies more efficiently prior to introducing new ones to raise their productivity, and that all the three components should be considered to evaluate their productivity more correctly.

• The Journal of Fisheries Business Administration
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• v.45 no.1
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• pp.63-77
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• 2014

This study is to estimate the recent changes in total factor productivity of 15 Korean adjacent water fisheries based on Malmquist productivity indices. The study adopted both input and output oriented productivity measures utilizing a hyperbola distance function. In addition to this point, the study also calculated the 95% confidence interval for the various components of the productivities in order to access the statistical significance of estimates using 2000 times of re-sampling process through the smoothed bootstraping. The results of the study showed us that there was 18% reduction in the overall total factor productivity during the study period from 2007 to 2011, which turned out to be 5% of annual decrease in productivity. The study found that the main reason of this decrease in total productivity is about 22% downward shift of a fisheries production function due to recent conditions of a devastated fishing ground. When we evaluated the statistical significance of changes in technical efficiency combining both pure technical and scale efficiency based on the 95% confidence intervals, we could not find any evidence of changes in those components of total factor productivity. When we accessed the productivity of the each of 15 adjacent water fisheries methods, only the large danish seine fisheries showed us about 7% increase in productivity. Even though the large trawling and the large tow-boat trawling revealed no changes in productivity, all of the other 12 fisheries suffered the decreases in productivities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.298-309
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• 2016

To increase the productivity and efficiency of public enterprises, privatization has been carried out in a number of different ways. However, the change in productivity and efficiency of privatization have not been validated. Therefore, it is important to investigate the change in productivity and efficiency of privatization. In this study, the impact of privatization was analyzed using the indices of Malmquist analysis and DEA. According to the case studies in Korean privatization, there is no change in efficiency due to privatization. On the other hand, privatization influences the productivity of privatization. These results suggest that the purpose of privatization should be clarified before privatization

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.57-83
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• 2006

The purpose of this paper is to analyze the efficiency and productivity change and inefficiency cause of the korean non-life insurance companies of the before($1993{\sim}1996$) and after($1998{\sim}2004$) of IMF. we use DEA (Data Envelopment Analysis) model to measure company efficiency and MPI(Malmquist productivity indices) to measure company productivity change and Tobit regression to analyze inefficiency cause. we utilize ten non-life insurance companies in korea and the time-series data for eleven from 1993 to 2004 except 1997. The empirical results show the following findings. First, total cost efficiency shows that the after of IMF decrease of 3.7% over the before of IMF and MPI change indicates that the after of IMF increase 7.7% over the before IMF. Second, the results of Tobit regression to analysis the cause of inefficiency show that total cost efficiency is positively related invested assets, acquisition expenses ratio, collection expenses ratio and is negatively related solicitors ratio, personnel expenses ratio, land & buildings expenses ratio, loss ratio, net operating expenses ratio. Especially inefficiency of small-to-mid sized companies is main cause of total cost efficiency of non-life insurance companies in korea. Small-to-mid sized companies endeavored various aspects of business strategies.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.203-215
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• 2013

This study calculated meta Malmquist indices and their bootstraped estimates and then decomposed them into technical efficiency change(TEC), technology change(TC), pure technology catch up(PTCU), frontoer catch up(FCU), using annual data set of general hospitals from year 2007 to 2011 collected by Korean Hospital Association and then analyzed productivity change and technology gap of Korean general hospitals. The results and implications were as follows below. First, public general hospitals showed higher meta technical efficiencies than private general hospitals while exhibited lower technology gap ratio which meant a few large private general hospitals led the whole general hospitals. Second, group productivity of private general hospitals increased larger than public general hospitals due to the differences of PTCU rather than FCU. But, there was no statistically significant differences for technical efficiency, productivity change, technology gap. Thus, public general hospitals played the same role as the private general hospitals in terms of the number of patients treated. But, considering financial hardships of public general hospitals, public hospitals needed to share and learn medical and managerial skills of the best practice of private general hospitals.